{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Treated Observations', 'Control Observations']\n"
     ]
    },
    {
     "data": {
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       "      <th>Unnamed: 0</th>\n",
       "      <th>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>source</th>\n",
       "      <th>id</th>\n",
       "      <th>replication</th>\n",
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       "      <th>orig2020-02</th>\n",
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       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
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       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
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       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
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       "      <td>https://ideas.repec.org/a/aea/aejpol/v6y2014i3...</td>\n",
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       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
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       "      <td>342</td>\n",
       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
       "      <td>https://ideas.repec.org/a/ucp/jpolec/doi10.108...</td>\n",
       "      <td>https://ideas.repec.org/a/ucp/jpolec/doi10.108...</td>\n",
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       "      <th>322</th>\n",
       "      <td>61</td>\n",
       "      <td>3</td>\n",
       "      <td>248</td>\n",
       "      <td>wiki nr</td>\n",
       "      <td>25</td>\n",
       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
       "      <td>https://ideas.repec.org/a/sae/pubfin/v44y2016i...</td>\n",
       "      <td>https://ideas.repec.org/a/ucp/jlawec/v52y2009i...</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.411765</td>\n",
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       "      <td>0</td>\n",
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       "      <th>323</th>\n",
       "      <td>66</td>\n",
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       "      <td>110</td>\n",
       "      <td>bob nr</td>\n",
       "      <td>324</td>\n",
       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
       "      <td>https://logec.repec.org/scripts/paperstat.pf?h...</td>\n",
       "      <td>https://ideas.repec.org/a/wly/hlthec/v19y2010i...</td>\n",
       "      <td>https://ideas.repec.org/a/eee/jhecon/v22y2003i...</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.490231</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>324 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  level_0  index   source   id  \\\n",
       "0           258        0     31   bob nr  329   \n",
       "1           284        2    172   bob nr  323   \n",
       "2           272        2    207   bob nr  130   \n",
       "3           273        3    279   new nr   71   \n",
       "4           227        1    114   bob nr  280   \n",
       "..          ...      ...    ...      ...  ...   \n",
       "319          62        4    297   new nr   21   \n",
       "320         290        0    190   bob nr  131   \n",
       "321          64        1     35   bob nr  342   \n",
       "322          61        3    248  wiki nr   25   \n",
       "323          66        3    110   bob nr  324   \n",
       "\n",
       "                                           replication  \\\n",
       "0    https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "1    https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "2    https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "3    https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "4    https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "..                                                 ...   \n",
       "319  https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "320  https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "321  https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "322  https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "323  https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "\n",
       "                                              original  \\\n",
       "0    https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "1    https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "2    https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "3    https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "4    https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "..                                                 ...   \n",
       "319  https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "320  https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "321  https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "322  https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "323  https://logec.repec.org/scripts/paperstat.pf?h...   \n",
       "\n",
       "                              repec handle replication  \\\n",
       "0    https://ideas.repec.org/a/oup/rfinst/v23y2010i...   \n",
       "1    https://ideas.repec.org/a/aea/aejmac/v2y2010i4...   \n",
       "2    https://ideas.repec.org/a/aea/aecrev/v100y2010...   \n",
       "3    https://ideas.repec.org/a/taf/applec/v50y2018i...   \n",
       "4    https://ideas.repec.org/a/eee/jhecon/v12y1993i...   \n",
       "..                                                 ...   \n",
       "319  https://ideas.repec.org/a/eee/eneeco/v82y2019i...   \n",
       "320  https://ideas.repec.org/a/aea/aecrev/v100y2010...   \n",
       "321  https://ideas.repec.org/a/ucp/jpolec/doi10.108...   \n",
       "322  https://ideas.repec.org/a/sae/pubfin/v44y2016i...   \n",
       "323  https://ideas.repec.org/a/wly/hlthec/v19y2010i...   \n",
       "\n",
       "                                 repec handle original  orig2020-02  ...  \\\n",
       "0    https://ideas.repec.org/a/ucp/jpolec/v106y1998...           39  ...   \n",
       "1    https://ideas.repec.org/a/aea/aecrev/v98y2008i...            4  ...   \n",
       "2    https://ideas.repec.org/a/aea/aecrev/v83y1993i...           10  ...   \n",
       "3    https://ideas.repec.org/a/tpr/restat/v90y2008i...            7  ...   \n",
       "4    https://ideas.repec.org/a/eee/jhecon/v10y1991i...            0  ...   \n",
       "..                                                 ...          ...  ...   \n",
       "319  https://ideas.repec.org/a/aea/aejpol/v6y2014i3...            0  ...   \n",
       "320  https://ideas.repec.org/a/aea/aecrev/v94y2004i...            3  ...   \n",
       "321  https://ideas.repec.org/a/ucp/jpolec/doi10.108...            1  ...   \n",
       "322  https://ideas.repec.org/a/ucp/jlawec/v52y2009i...            0  ...   \n",
       "323  https://ideas.repec.org/a/eee/jhecon/v22y2003i...            2  ...   \n",
       "\n",
       "     repl2020-02  repl2020-03  repl2020-04  repl2020-05  repl2020-06  \\\n",
       "0              4            7           10           10            3   \n",
       "1              4            7            7            5            3   \n",
       "2              2            3            6            1            1   \n",
       "3              1            1            1            0            0   \n",
       "4              0            0            0            0            3   \n",
       "..           ...          ...          ...          ...          ...   \n",
       "319            0            0            0            0            0   \n",
       "320            1            2            4            4            0   \n",
       "321            2            1            3            0            1   \n",
       "322            0            0            0            0            1   \n",
       "323            0            0            2            0            2   \n",
       "\n",
       "     repl2020-07   1         2  clusters  randomized  \n",
       "0              7  41  0.172306        64           1  \n",
       "1              5  31  0.798258        48           1  \n",
       "2              2  15  0.343558        58           1  \n",
       "3              1   4  0.062802        58           1  \n",
       "4              1   4  0.636364        14           1  \n",
       "..           ...  ..       ...       ...         ...  \n",
       "319            1   1  1.000000         0           0  \n",
       "320            2  13  0.614833        40           0  \n",
       "321            1   8  0.933884        35           0  \n",
       "322            0   1  1.411765         0           0  \n",
       "323            3   7  0.490231        35           0  \n",
       "\n",
       "[324 rows x 26 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#step 1: this is the data from the period February 2020-July 2020 which was used to cluster and randomize the pairs\n",
    "import pandas as pd\n",
    "\n",
    "data=pd.ExcelFile('RePEc experiment - Treatment and Control Pairs 06 08 2020.xlsx')\n",
    "print(data.sheet_names)\n",
    "df1 = data.parse('Treated Observations')\n",
    "df2 = data.parse('Control Observations')\n",
    "df2['new index']=list(range(161,324))\n",
    "df2=df2.set_index('new index')\n",
    "df=pd.concat([df1,df2], axis=0)\n",
    "data=df\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:rfinst:v:23:y:2010:i:2:p:467-486'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['replication'].loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_567091/673480983.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  data['clusters'].loc[data['clusters']==68]=67\n"
     ]
    }
   ],
   "source": [
    "# the last cluster has only 2 observations so we merge with 67\n",
    "data['clusters'].loc[data['clusters']==68]=67"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.503067484662576\n",
      "75.86335403726709\n",
      "3.607361963190184\n",
      "16.658385093167702\n"
     ]
    }
   ],
   "source": [
    "# for nr of cites by bottom half and top half clusters - this is for answer to referee in Rev II\n",
    "import numpy as np\n",
    "ppp=data[data['clusters']<34] \n",
    "print(np.mean(ppp[0])) #citations to originals\n",
    "ppp=data[data['clusters']>33] \n",
    "print(np.mean(ppp[0])) #citations to originals\n",
    "ppp=data[data['clusters']<34] \n",
    "print(np.mean(ppp[1])) #citations to replications\n",
    "ppp=data[data['clusters']>33] \n",
    "print(np.mean(ppp[1])) #citations to replications\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "161\n",
      "10.211180124223603\n",
      "41.993788819875775\n",
      "5.0\n",
      "17.0\n",
      "163\n",
      "9.975460122699387\n",
      "43.95705521472393\n",
      "5.0\n",
      "17.0\n"
     ]
    }
   ],
   "source": [
    "# table 1 - descriptive stats of the randomized\n",
    "ppp=data[data['randomized']==1] # early treated group\n",
    "print(len(ppp))\n",
    "print(ppp[1].sum(axis=0)/len(ppp)) # replications \n",
    "print(ppp[0].sum(axis=0)/len(ppp)) # originals\n",
    "print(ppp[1].median())\n",
    "print(ppp[0].median())\n",
    "# controls\n",
    "ppp=data[data['randomized']==0] # late treated group\n",
    "print(len(ppp))\n",
    "print(ppp[1].sum(axis=0)/len(ppp)) # replications\n",
    "print(ppp[0].sum(axis=0)/len(ppp)) # originals\n",
    "print(ppp[1].median())\n",
    "print(ppp[0].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "159\n",
      "161\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a beautiful histogram of visits to replicated papers\n",
    "import numpy as np\n",
    "from matplotlib import pylab as pl\n",
    "import random\n",
    "import numpy\n",
    "#from matplotlib import pyplot\n",
    "\n",
    "ppp=data[data['randomized']==1]\n",
    "x=ppp[1]\n",
    "ppp=data[data['randomized']==0]\n",
    "y=ppp[1]\n",
    "\n",
    "bins=list(range(0,80,5))\n",
    "\n",
    "dataOne = x[x<80]\n",
    "dataTwo = y[y<80]\n",
    "print(len(dataOne))\n",
    "print(len(dataTwo))\n",
    "\n",
    "#hN = pl.hist(dataTwo, bins=bins , orientation='horizontal', rwidth=0.8, label='Group 2', color='blue',weights=np.zeros_like(dataTwo) + 1. / dataTwo.size)\n",
    "hN = pl.hist(dataTwo, bins=bins , orientation='horizontal', rwidth=0.8, label='Group 2', color='grey',weights=np.zeros_like(dataTwo) + 1. / y.size)\n",
    "# to make relative frequencies, we use weights and these weights are based on all observations (y) not\n",
    "# just those whom we show (dataTwo)\n",
    "\n",
    "#hS = pl.hist(dataOne, bins=bins , orientation='horizontal', color='black' ,    rwidth=0.8, label='Group 1', weights=np.zeros_like(dataOne) + 1. / dataOne.size)\n",
    "hS = pl.hist(dataOne, bins=bins , orientation='horizontal', color='black' , rwidth=0.8, label='Group 1', weights=np.zeros_like(dataOne) + 1. / x.size)\n",
    "\n",
    "for p in hS[2]:\n",
    "    p.set_width( - p.get_width())\n",
    "\n",
    "xmin = min([ min(w.get_width() for w in hS[2]), \n",
    "                min([w.get_width() for w in hN[2]]) ])\n",
    "xmin = np.floor(xmin)\n",
    "xmax = max([ max(w.get_width() for w in hS[2]), \n",
    "                max([w.get_width() for w in hN[2]]) ])\n",
    "xmax = np.ceil(xmax)\n",
    "range1 = xmax - xmin\n",
    "delta = 0.0 * range1\n",
    "#pl.xlim([xmin - delta, xmax + delta])\n",
    "pl.xlim([-0.5, 0.5])\n",
    "xt = pl.xticks()\n",
    "n = xt[0]\n",
    "s = ['%.2f'%abs(i) for i in n]\n",
    "pl.xticks(n, s)\n",
    "#pl.legend(loc='best')\n",
    "pl.text(-0.38,70, 'Group 1', fontsize=12, color='black')\n",
    "pl.text(0.22,70, 'Group 2', fontsize=12, color='grey')\n",
    "\n",
    "pl.axvline(0.0)\n",
    "pl.grid(which='major',  linestyle='--')\n",
    "pl.ylabel('Visits') \n",
    "pl.xlabel('Frequency') \n",
    "\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "156\n",
      "159\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making beautiful histogram of visits to originals\n",
    "import numpy as np\n",
    "from matplotlib import pylab as pl\n",
    "import random\n",
    "import numpy\n",
    "#from matplotlib import pyplot\n",
    "\n",
    "ppp=data[data['randomized']==1]\n",
    "x=ppp[0]\n",
    "ppp=data[data['randomized']==0]\n",
    "y=ppp[0]\n",
    "\n",
    "bins=list(range(0,200,10))\n",
    "\n",
    "dataOne = x[x<200]\n",
    "dataTwo = y[y<200]\n",
    "print(len(dataOne))\n",
    "print(len(dataTwo))\n",
    "\n",
    "hN = pl.hist(dataTwo, bins=bins , orientation='horizontal', rwidth=0.8, label='Group 2', color='grey',weights=np.zeros_like(dataTwo) + 1. / y.size)\n",
    "\n",
    "hS = pl.hist(dataOne, bins=bins , orientation='horizontal', color='black' ,\n",
    "    rwidth=0.8, label='Group 1', weights=np.zeros_like(dataOne) + 1. / x.size)\n",
    "\n",
    "for p in hS[2]:\n",
    "    p.set_width( - p.get_width())\n",
    "\n",
    "xmin = min([ min(w.get_width() for w in hS[2]), \n",
    "                min([w.get_width() for w in hN[2]]) ])\n",
    "xmin = np.floor(xmin)\n",
    "xmax = max([ max(w.get_width() for w in hS[2]), \n",
    "                max([w.get_width() for w in hN[2]]) ])\n",
    "xmax = np.ceil(xmax)\n",
    "range1 = xmax - xmin\n",
    "delta = 0.0 * range1\n",
    "#pl.xlim([xmin - delta, xmax + delta])\n",
    "pl.xlim([-0.35, 0.35])\n",
    "xt = pl.xticks()\n",
    "n = xt[0]\n",
    "s = ['%.3f'%abs(i) for i in n]\n",
    "pl.xticks(n, s)\n",
    "#pl.legend(loc='best')\n",
    "pl.text(-0.30,175, 'Group 1', fontsize=12, color='black')\n",
    "pl.text(0.10,175, 'Group 2', fontsize=12, color='grey')\n",
    "\n",
    "pl.axvline(0.0)\n",
    "pl.grid(which='major',  linestyle='--')\n",
    "pl.ylabel('Visits') \n",
    "pl.xlabel('Frequency') \n",
    "pl.rc('axes', axisbelow=True)\n",
    "pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_567091/3780752079.py:160: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  datesorig=pd.Series(aa[::3])\n",
      "/tmp/ipykernel_567091/3780752079.py:161: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  downloadsorig=pd.Series(aa[1::3])\n",
      "/tmp/ipykernel_567091/3780752079.py:162: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  visitsorig=pd.Series(aa[2::3])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   "source": [
    "# extracting logs from repec server\n",
    "import urllib.request # import the program needed to open websites \n",
    "from urllib.request import Request, urlopen # get the request command from the urllib.request package                                         \n",
    "from bs4 import BeautifulSoup # the beautifulsoup program allows you to scrape based on the html code \n",
    "\n",
    "ov=data.reset_index(drop=True)\n",
    "\n",
    "replvisits=pd.DataFrame()\n",
    "origvisits=pd.DataFrame()\n",
    "visitsratio=[]\n",
    "downratio=[]\n",
    "\n",
    "visitst=[None,None,None,None,None, None,None,None,None,None,None, None,None,None,None,None,None, None]\n",
    "df = pd.DataFrame({'visits':visitst})\n",
    "datest=pd.Series([ '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07' ])\n",
    "df=df.set_index(datest)\n",
    "df=df.loc[[  '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07'  ], ]\n",
    "df=df.T\n",
    "templ=df\n",
    "\n",
    "visitst=[None,None,None,None,None, None,None,None,None,None,None, None,None,None,None,None,None, None]\n",
    "df = pd.DataFrame({'visitsorig':visitst})\n",
    "datest=pd.Series([  '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07'  ])\n",
    "df=df.set_index(datest)\n",
    "df=df.loc[[  '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07'  ], ]\n",
    "df=df.T\n",
    "templorig=df\n",
    "#for j in range(150,160): \n",
    "for j in range(0,len(ov)): \n",
    "    print(j)\n",
    "    repl=ov.loc[j,'replication']\n",
    "    # below we have a number of urls that needed repairs\n",
    "    if repl[len(repl)-1]==\".\":\n",
    "        repl=repl[0:len(repl)-1]\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:uwp:jhriss:v:42:y:2007:i:4:p:919-932' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:uwp:jhriss:v:42:y:2007:i4:p919-932'\n",
    "    if \"jpolec:do:i\" in repl:\n",
    "        repl=repl.replace(\"jpolec:do:i\",\"jpolec:doi\").replace(\"-\",\"/\")\n",
    "    if \"jpolec:doi\" in repl and not \"jpolec:doi:\" in repl:\n",
    "        repl=repl.replace(\"jpolec:doi\",\"jpolec:doi:\")\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:apsrev:v:94:y:2000:i:3:p:653-663_22' in repl:\n",
    "        repl=\"https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:apsrev:v:94:y:2000:i:03:p:653-663_22\"\n",
    "    if 'qjecon' in repl :\n",
    "        repl=repl+\".&donerecode=1\"\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:restud:v:85:y:2018:i:2:p:999-1028' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=repec:oup:restud:v:85:y:2018:i:2:p:999-1028.&donerecode=1'\n",
    "\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:64:y:2010:i:2:p:339-356_00' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:64:y:2010:i:02:p:339-356_00'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:59:y:2005:i:3:p:593-629_05' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:59:y:2005:i:03:p:593-629_05'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:66:y:2012:i:2:p:329-358_00' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:66:y:2012:i:02:p:329-358_00'\n",
    "\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:ucp:jlawec:v:48:y:2005:i:2:p:653-75' in repl:\n",
    "        repl=\"https://logec.repec.org/scripts/paperstat.pf?h=RePEc:ucp:jlawec:y:2005:v:48:i:2:p:653-75\"           \n",
    "\n",
    "    if \"https://logec.repec.org/scripts/paperstat.pf?h=RePEc:bes:jnlbes:v:27:y:2009:i:4:p:492-503\" in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:bes:jnlbes:v:27:i:4:y:2009:p:492-503'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:restud:v:85:y:2018:i:3:p:1577-1608' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=repec:oup:restud:v:85:y:2018:i:3:p:1577-1608.&donerecode=1'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:gam:jlawss:v:7:y:2018:i:4:p:34-d173843' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:gam:jlawss:v:7:y:2018:i:4:p:34-:d:173843'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:gam:jlawss:v:3:y:2014:i:2:p:327-352d37146' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:gam:jlawss:v:3:y:2014:i:2:p:327-352:d:37146'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:apsrev:v:102:y:2008:i:1:p:107-123_08' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:apsrev:v:102:y:2008:i:01:p:107-123_08'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:128:y:2013:i:1:p:105-164.&donerecode=1' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:128:y:2013:i:1:p:105-164'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:126:y:2011:i:3:p:1485-1538.&donerecode=1' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:126:y:2011:i:3:p:1485-1538'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:126:y:2011:i:4:p:2117-2123.&donerecode=1' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:126:y:2011:i:4:p:2117-2123'\n",
    "\n",
    "\n",
    "\n",
    "    req = Request(repl, headers={'User-Agent': 'Mozilla/5.0'})#the url we need to load\n",
    "    tt = urlopen(req).read() # read the website from the req variable and save it into the variable tt\n",
    "    soup = BeautifulSoup(tt,\"html5lib\") # read the variable tt using beautifulsoup and save it in the variable soup\n",
    "    a=soup.find_all(\"td\",class_=\"statnumber rightmost\")\n",
    "    aa=[]\n",
    "    for i in a:\n",
    "        aa+=[i.text]\n",
    "    dates=pd.Series(aa[::3])\n",
    "    downloads=pd.Series(aa[1::3])\n",
    "    visits=pd.Series(aa[2::3])\n",
    "    visits=pd.to_numeric(visits)\n",
    "    downloads=pd.to_numeric(downloads)\n",
    "\n",
    "\n",
    "\n",
    "    df = pd.DataFrame({'visits':visits})\n",
    "    df=df.set_index(dates)\n",
    "    df=df.reindex([  '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07'  ])\n",
    "    df=df.T\n",
    "\n",
    "    replvisits = pd.concat([replvisits,df],ignore_index=True)\n",
    "    if len(df)==0:\n",
    "        print('warning',i)\n",
    "\n",
    "\n",
    "\n",
    "    repl=ov.loc[j,'original']\n",
    "    if repl[len(repl)-1]==\".\":\n",
    "        repl=repl[0:len(repl)-1]\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:uwp:jhriss:v:38:y:2003:i:3:p:701-721' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:uwp:jhriss:v:38:y:2003:i:3:p701-721'\n",
    "    if \"jpolec:do:i\" in repl:\n",
    "        repl=repl.replace(\"jpolec:do:i\",\"jpolec:doi\").replace(\"-\",\"/\")\n",
    "    if \"jpolec:doi\" in repl and not \"jpolec:doi:\" in repl:\n",
    "        repl=repl.replace(\"jpolec:doi\",\"jpolec:doi:\")\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:ucp:jpolec:doi10.1086/665011' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:ucp:jpolec:doi:10.1086/665011'\n",
    "\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:restud:v:85:y:2018:i:3:p:1577-1608' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=repec:oup:restud:v:85:y:2018:i:3:p:1577-1608.&donerecode=1'\n",
    "\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:restud:v:85:y:2018:i:2:p:999-1028' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=repec:oup:restud:v:85:y:2018:i:2:p:999-1028.&donerecode=1'\n",
    "\n",
    "\n",
    "    if 'qjecon' in repl:\n",
    "        repl=repl+\".&donerecode=1\"\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:64:y:2010:i:2:p:339-356_00' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:64:y:2010:i:02:p:339-356_00'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:59:y:2005:i:3:p:593-629_05' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:59:y:2005:i:03:p:593-629_05'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:66:y:2012:i:2:p:329-358_00' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:intorg:v:66:y:2012:i:02:p:329-358_00'\n",
    "\n",
    "\n",
    "    if \"https://logec.repec.org/scripts/paperstat.pf?h=RePEc:bes:jnlbes:v:27:y:2009:i:4:p:492-503\" in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:bes:jnlbes:v:27:i:4:y:2009:p:492-503'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:gam:jlawss:v:7:y:2018:i:4:p:34-d173843' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:gam:jlawss:v:7:y:2018:i:4:p:34-:d:173843'\n",
    "\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:gam:jlawss:v:3:y:2014:i:2:p:327-352d37146' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:gam:jlawss:v:3:y:2014:i:2:p:327-352:d:37146'\n",
    "\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:apsrev:v:94:y:2000:i:3:p:653-663_22' in repl:\n",
    "        repl=\"https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:apsrev:v:94:y:2000:i:03:p:653-663_22\"\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:apsrev:v:102:y:2008:i:1:p:107-123_08' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:cup:apsrev:v:102:y:2008:i:01:p:107-123_08'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:ucp:jlawec:v:48:y:2005:i:2:p:653-75' in repl:\n",
    "        repl=\"https://logec.repec.org/scripts/paperstat.pf?h=RePEc:ucp:jlawec:y:2005:v:48:i:2:p:653-75\"\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:128:y:2013:i:1:p:105-164.&donerecode=1' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:128:y:2013:i:1:p:105-164'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:126:y:2011:i:3:p:1485-1538.&donerecode=1' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:126:y:2011:i:3:p:1485-1538'\n",
    "    if 'https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:126:y:2011:i:4:p:2117-2123.&donerecode=1' in repl:\n",
    "        repl='https://logec.repec.org/scripts/paperstat.pf?h=RePEc:oup:qjecon:v:126:y:2011:i:4:p:2117-2123'\n",
    "\n",
    "    req = Request(repl, headers={'User-Agent': 'Mozilla/5.0'})#the url we need to load\n",
    "    tt = urlopen(req).read() # read the website from the req variable and save it into the variable tt\n",
    "    soup = BeautifulSoup(tt,\"html5lib\") # read the variable tt using beautifulsoup and save it in the variable soup\n",
    "    a=soup.find_all(\"td\",class_=\"statnumber rightmost\")\n",
    "    aa=[]\n",
    "    for i in a:\n",
    "        aa+=[i.text]\n",
    "    datesorig=pd.Series(aa[::3])\n",
    "    downloadsorig=pd.Series(aa[1::3])\n",
    "    visitsorig=pd.Series(aa[2::3])\n",
    "    visitsorig=pd.to_numeric(visitsorig)\n",
    "    downloadsorig=pd.to_numeric(downloadsorig)\n",
    "    downloadsorig=downloadsorig[len(datesorig)-len(dates):len(datesorig)]\n",
    "    visitsorig=visitsorig[max(0,len(datesorig)-len(dates)):len(datesorig)]\n",
    "    datesorig=datesorig[max(0,len(datesorig)-len(dates)):len(datesorig)]\n",
    "\n",
    "    if sum(visitsorig)>0:\n",
    "        visitsratio=visitsratio+[sum(visits)/sum(visitsorig)]\n",
    "    else:\n",
    "        visitsratio=visitsratio+[None]\n",
    "\n",
    "    if sum(downloadsorig)>0:\n",
    "        downratio=downratio+[sum(downloads)/sum(downloadsorig)]\n",
    "    else:\n",
    "        downratio=downratio+[None]\n",
    "\n",
    "\n",
    "    df = pd.DataFrame({'visitsorig':visitsorig})\n",
    "    df=df.set_index(datesorig)\n",
    "    df=df.reindex([  '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07'  ])\n",
    "    df=df.T\n",
    "    origvisits = pd.concat([origvisits,df],ignore_index=True)\n",
    "    if len(df)==0:\n",
    "        print('warning',i)\n",
    "\n",
    "visitsorig=pd.to_numeric(visitsorig)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to save the data [ this step can be skipped if one wants to use the data i saved already]\n",
    "origvisits.to_csv('LogEcvisitstooriginals.csv')\n",
    "origvisits.to_pickle('LogEcvisitstooriginals')\n",
    "replvisits.to_csv('LogEcvisitstoreplications.csv')\n",
    "replvisits.to_pickle('LogEcvisitstoreplications')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to get the saved data\n",
    "origvisits=pd.read_pickle('LogEcvisitstooriginals')\n",
    "replvisits=pd.read_pickle('LogEcvisitstoreplications')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2020-02</th>\n",
       "      <th>2020-03</th>\n",
       "      <th>2020-04</th>\n",
       "      <th>2020-05</th>\n",
       "      <th>2020-06</th>\n",
       "      <th>2020-07</th>\n",
       "      <th>2020-08</th>\n",
       "      <th>2020-09</th>\n",
       "      <th>2020-10</th>\n",
       "      <th>2020-11</th>\n",
       "      <th>2020-12</th>\n",
       "      <th>2021-01</th>\n",
       "      <th>2021-02</th>\n",
       "      <th>2021-03</th>\n",
       "      <th>2021-04</th>\n",
       "      <th>2021-05</th>\n",
       "      <th>2021-06</th>\n",
       "      <th>2021-07</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    2020-02  2020-03  2020-04  2020-05  2020-06  2020-07  2020-08  2020-09  \\\n",
       "55      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "    2020-10  2020-11  2020-12  2021-01  2021-02  2021-03  2021-04  2021-05  \\\n",
       "55      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "\n",
       "    2021-06  2021-07  \n",
       "55      NaN      NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# note - # 55 is missing for the originals - the page no longer exists on LogEc\n",
    "origvisits[origvisits['2020-06'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#so let's exclude this for both originals and replications\n",
    "replvisits=replvisits[origvisits['2020-06'].notnull()].reset_index(drop=True)\n",
    "data=data[origvisits['2020-06'].notnull()].reset_index(drop=True)\n",
    "origvisits=origvisits[origvisits['2020-06'].notnull()].reset_index(drop=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2020-02:orig2020-02,2020-03:orig2020-03,2020-04:orig2020-04,2020-05:orig2020-05,2020-06:orig2020-06,2020-07:orig2020-07,2020-08:orig2020-08,2020-09:orig2020-09,2020-10:orig2020-10,2020-11:orig2020-11,2020-12:orig2020-12,2021-01:orig2021-01,2021-02:orig2021-02,2021-03:orig2021-03,2021-04:orig2021-04,2021-05:orig2021-05,2021-06:orig2021-06,2021-07:orig2021-07'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will rename the column names, adding orig to them\n",
    "# here we create the changed names\n",
    "strings=[]\n",
    "for i in [ '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07' ]:\n",
    "    strings=strings +[i+':orig'+i]\n",
    "\n",
    "stri=strings[0]\n",
    "for i in range(1, len(strings)):\n",
    "    stri=stri+',' + strings[i]\n",
    "stri\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here we change the column names in the dataframes\n",
    "# not sure anymore why i did this\n",
    "origvisits = origvisits.rename(columns={\"2020-02\":\"orig2020-02\",\"2020-03\":\"orig2020-03\",\"2020-04\":\"orig2020-04\",\"2020-05\":\"orig2020-05\",\"2020-06\":\"orig2020-06\",\"2020-07\":\"orig2020-07\",\"2020-08\":\"orig2020-08\",\"2020-09\":\"orig2020-09\",\"2020-10\":\"orig2020-10\",\"2020-11\":\"orig2020-11\",\"2020-12\":\"orig2020-12\",\"2021-01\":\"orig2021-01\",\"2021-02\":\"orig2021-02\",\"2021-03\":\"orig2021-03\",\"2021-04\":\"orig2021-04\",\"2021-05\":\"orig2021-05\",\"2021-06\":\"orig2021-06\",\"2021-07\":\"orig2021-07\"})\n",
    "replvisits = replvisits.rename(columns={\"2020-02\":\"orig2020-02\",\"2020-03\":\"orig2020-03\",\"2020-04\":\"orig2020-04\",\"2020-05\":\"orig2020-05\",\"2020-06\":\"orig2020-06\",\"2020-07\":\"orig2020-07\",\"2020-08\":\"orig2020-08\",\"2020-09\":\"orig2020-09\",\"2020-10\":\"orig2020-10\",\"2020-11\":\"orig2020-11\",\"2020-12\":\"orig2020-12\",\"2021-01\":\"orig2021-01\",\"2021-02\":\"orig2021-02\",\"2021-03\":\"orig2021-03\",\"2021-04\":\"orig2021-04\",\"2021-05\":\"orig2021-05\",\"2021-06\":\"orig2021-06\",\"2021-07\":\"orig2021-07\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# computing visit sums by paper\n",
    "origsum=origvisits.sum(axis=1)\n",
    "replsum=replvisits.sum(axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "orig2020-02     866.0\n",
      "orig2020-03    1444.0\n",
      "orig2020-04    1332.0\n",
      "orig2020-05    1225.0\n",
      "orig2020-06    1057.0\n",
      "orig2020-07     831.0\n",
      "orig2020-08     873.0\n",
      "orig2020-09    1041.0\n",
      "orig2020-10    1152.0\n",
      "orig2020-11    1316.0\n",
      "orig2020-12    1270.0\n",
      "orig2021-01    1021.0\n",
      "orig2021-02    1017.0\n",
      "orig2021-03    1361.0\n",
      "orig2021-04    1228.0\n",
      "orig2021-05    1131.0\n",
      "orig2021-06     888.0\n",
      "orig2021-07     698.0\n",
      "dtype: float64\n",
      "orig2020-02     965.0\n",
      "orig2020-03    1317.0\n",
      "orig2020-04    1304.0\n",
      "orig2020-05    1385.0\n",
      "orig2020-06    1319.0\n",
      "orig2020-07     875.0\n",
      "orig2020-08     869.0\n",
      "orig2020-09     999.0\n",
      "orig2020-10    1380.0\n",
      "orig2020-11    1291.0\n",
      "orig2020-12    1260.0\n",
      "orig2021-01    1048.0\n",
      "orig2021-02    1041.0\n",
      "orig2021-03    1334.0\n",
      "orig2021-04    1211.0\n",
      "orig2021-05    1117.0\n",
      "orig2021-06     989.0\n",
      "orig2021-07     687.0\n",
      "dtype: float64\n",
      "orig2020-02    283\n",
      "orig2020-03    319\n",
      "orig2020-04    307\n",
      "orig2020-05    283\n",
      "orig2020-06    205\n",
      "orig2020-07    245\n",
      "orig2020-08    241\n",
      "orig2020-09    235\n",
      "orig2020-10    308\n",
      "orig2020-11    336\n",
      "orig2020-12    276\n",
      "orig2021-01    272\n",
      "orig2021-02    267\n",
      "orig2021-03    354\n",
      "orig2021-04    377\n",
      "orig2021-05    336\n",
      "orig2021-06    239\n",
      "orig2021-07    192\n",
      "dtype: int64\n",
      "orig2020-02    229\n",
      "orig2020-03    262\n",
      "orig2020-04    279\n",
      "orig2020-05    252\n",
      "orig2020-06    351\n",
      "orig2020-07    253\n",
      "orig2020-08    242\n",
      "orig2020-09    206\n",
      "orig2020-10    274\n",
      "orig2020-11    287\n",
      "orig2020-12    234\n",
      "orig2021-01    242\n",
      "orig2021-02    245\n",
      "orig2021-03    306\n",
      "orig2021-04    336\n",
      "orig2021-05    328\n",
      "orig2021-06    247\n",
      "orig2021-07    171\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# computing monthly sums, seperately for group 1 (1) and group 2 (0) \n",
    "print(origvisits[data['randomized']==1].sum(axis=0))\n",
    "print(origvisits[data['randomized']==0].sum(axis=0))\n",
    "print(replvisits[data['randomized']==1].sum(axis=0))\n",
    "print(replvisits[data['randomized']==0].sum(axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 2.0, 12.0, 23.0, 34.0, 10.0, 13.0, 15.0, 12.0, 12.0, 12.0, 9.0, 9.0]\n",
      "[0.0, 0.0, 0.0, 2.0, 0.0, 3.0, 1.0, 2.0, 5.0, 6.0, 2.0, 5.0, 4.0, 1.0, 2.0, 4.0, 5.0, 2.0]\n",
      "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 9.0, 25.0, 19.0, 10.0, 9.0]\n",
      "[1.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 3.0, 4.0, 4.0, 0.0, 1.0]\n"
     ]
    }
   ],
   "source": [
    "# loading the data on clicks, which came as monthly csv from Christian\n",
    "import pandas as pd\n",
    "totaltreatedto=[]\n",
    "totaltreatedback=[]\n",
    "totalnottreatedto=[]\n",
    "totalnottreatedback=[]\n",
    "for i in [2002,2003,2004,2005,2006,2007,2008, 2009,2010,2011,2012,2101,2102,2103,2104,2105,2106,2107]:\n",
    "    df = pd.read_csv(\n",
    "        str(i)+'.txt', sep=\" \",header=None)\n",
    "    \n",
    "    # print(df.loc[55])\n",
    "    # note the link to loc[55] with missing original counts never has been used so it does not affect counts of total clicks!\n",
    "    # monthly totals were available in Christan's file on line 161 and 325\n",
    "    totaltreatedto+=[float(df.loc[161][2].replace(\",\",\"\"))]\n",
    "    totaltreatedback+=[float(df.loc[161][4].replace(\",\",\"\"))]\n",
    "    totalnottreatedto+=[float(df.loc[325][2].replace(\",\",\"\"))]\n",
    "    totalnottreatedback+=[float(df.loc[325][4].replace(\",\",\"\"))]\n",
    "print(totaltreatedto) # originals to treated group 1\n",
    "print(totaltreatedback)# treated to originals group 1\n",
    "print(totalnottreatedto) # originals to treated group 2\n",
    "print(totalnottreatedback)# treated to originals group 2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.1, 0.0, 0.0, 0.1, 0.0, 0.2, 1.2, 2.0, 2.6, 0.8, 1.3, 1.5, 0.9, 1.0, 1.1, 1.0, 1.3]\n",
      "[0.0, 0.3, 0.0, 0.0, 0.5, 0.0, 0.8, 5.4, 8.1, 11.3, 3.8, 5.0, 6.0, 3.5, 3.3, 3.7, 3.9, 4.9]\n"
     ]
    }
   ],
   "source": [
    "# NUMBERS DISCUSSED IN SECTION III (Measure I) \n",
    "\n",
    "# shares : clicks divided by visits to originals, clicks divided by visits to replications\n",
    "share=[]\n",
    "sharerepl=[]\n",
    "origsum=origvisits[data['randomized']==1].sum(axis=0)\n",
    "replsum=replvisits[data['randomized']==1].sum(axis=0)\n",
    "\n",
    "for i in range(0,len(origsum)):\n",
    "    share+= [round(100*totaltreatedto[i]/(origsum[i]-totaltreatedback[i]),1)]\n",
    "    # we subtract from original counts the ones comming from replications as these are likely returns\n",
    "    sharerepl+= [round(100*totaltreatedto[i]/(replsum[i]-totaltreatedto[i]),1)] # we want to see increase so we also do it as shareof visits \n",
    "    # from other sources\n",
    "print(share)\n",
    "print(sharerepl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n",
      "107.0\n",
      "42.0\n",
      "6750.0\n",
      "6793.0\n",
      "3932.0\n"
     ]
    }
   ],
   "source": [
    "# now focus on specific periods for Group 1\n",
    "# clicks versus visits to originals\n",
    "import numpy as np\n",
    "print(np.sum(totaltreatedto[0:6])) # before treatment\n",
    "print(np.sum(totaltreatedto[7:13])) # group 1 - we exclude august as not sure when treatment start for given paper\n",
    "print(np.sum(totaltreatedto[14:18])) # group 2 - we exclude march as not sure when treatment start for given paper\n",
    "print(np.sum(origsum[0:6])-np.sum(totaltreatedback[0:6]))\n",
    "print(np.sum(origsum[7:13])-np.sum(totaltreatedback[7:13]))\n",
    "print(np.sum(origsum[14:18])-np.sum(totaltreatedback[14:18]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.015751508906226997\n",
      "0.010681586978636826\n"
     ]
    }
   ],
   "source": [
    "# stage I overall stats treatment period I Group 1\n",
    "print(107/6793) # the 1.6% we mention in the abstract\n",
    "# stage I overall stats treatment period 2 Group 1\n",
    "print(42/3932)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n",
      "107.0\n",
      "42.0\n",
      "1640.0\n",
      "1587.0\n",
      "1102.0\n"
     ]
    }
   ],
   "source": [
    "# NUMBERS DISCUSSED IN SECTION III (Measure II) \n",
    "\n",
    "# clicks versus visits to the replications for Group 1\n",
    "import numpy as np\n",
    "print(np.sum(totaltreatedto[0:6]))\n",
    "print(np.sum(totaltreatedto[7:13]))\n",
    "print(np.sum(totaltreatedto[14:18]))\n",
    "print(np.sum(replsum[0:6])-np.sum(totaltreatedto[0:6]))\n",
    "print(np.sum(replsum[7:13])-np.sum(totaltreatedto[7:13]))\n",
    "print(np.sum(replsum[14:18])-np.sum(totaltreatedto[14:18]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06742281033396345\n",
      "0.038112522686025406\n"
     ]
    }
   ],
   "source": [
    "print(107/1587)\n",
    "print(42/1102)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.7, 2.1, 1.7, 1.0, 1.3]\n",
      "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.4, 0.0, 0.0, 0.0, 0.0, 3.0, 8.0, 6.1, 4.2, 5.6]\n"
     ]
    }
   ],
   "source": [
    "# NUMBERS DISCUSSED IN SECTION III (Measure I) \n",
    "\n",
    "# now focus on group 2\n",
    "share2=[]\n",
    "share2repl=[]\n",
    "\n",
    "origsum=origvisits[data['randomized']==0].sum(axis=0)\n",
    "replsum=replvisits[data['randomized']==0].sum(axis=0)\n",
    "\n",
    "for i in range(0,len(origsum)):\n",
    "    share2+= [round(100*totalnottreatedto[i]/(origsum[i]-totalnottreatedback[i]),1)]\n",
    "    share2repl+= [round(100*totalnottreatedto[i]/(replsum[i]-totalnottreatedto[i]),1)]    \n",
    "print(share2)\n",
    "print(share2repl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "1.0\n",
      "63.0\n",
      "7161.0\n",
      "7018.0\n",
      "3995.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(np.sum(totalnottreatedto[0:6]))\n",
    "print(np.sum(totalnottreatedto[7:13]))\n",
    "print(np.sum(totalnottreatedto[14:18]))\n",
    "print(np.sum(origsum[0:6])-np.sum(totalnottreatedback[0:6]))\n",
    "print(np.sum(origsum[7:13])-np.sum(totalnottreatedback[7:13]))\n",
    "print(np.sum(origsum[14:18])-np.sum(totalnottreatedback[14:18]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "63/3995"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "1.0\n",
      "63.0\n",
      "1626.0\n",
      "1487.0\n",
      "1019.0\n"
     ]
    }
   ],
   "source": [
    "# NUMBERS DISCUSSED IN SECTION III (Measure II) \n",
    "# group 2 relative to replication visits\n",
    "import numpy as np\n",
    "print(np.sum(totalnottreatedto[0:6]))\n",
    "print(np.sum(totalnottreatedto[7:13]))\n",
    "print(np.sum(totalnottreatedto[14:18]))\n",
    "print(np.sum(replsum[0:6])-np.sum(totalnottreatedto[0:6]))\n",
    "print(np.sum(replsum[7:13])-np.sum(totalnottreatedto[7:13]))\n",
    "print(np.sum(replsum[14:18])-np.sum(totalnottreatedto[14:18]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06182531894013739"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "63/1019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ijo6WnSuH7GYxJiYG48ePR5cuXXD37l3dQS3u7u5YsmSJoesjIhOXnZ1tskcOEpkDjjEgKQkwVD/k7+8PSZJw6dIlveW1a9eGv78/HB0diz2mpM3VDzebQgjdMisrK92yInI2LT/83A/LzMxE586d4eLigq1btyp+wI3sZnHp0qX47LPP8O677+odmdW8eXMkJCQYtDgiIiIiHx9gxgzDPJeHhwc6duyIZcuWlXu2tkGDBjh06JDesiNHjqBBgwYAgMqVKwO4fzxHkbNnz5b4XMeOHdP9/s8//+DKlSuoX79+qdkZGRkIDw+HnZ0dvvvuOzg4OJTrNcghe5/Fa9euISgoqNhye3t7i50iJyIiImVERgosWCAZbJ9FAPjkk08QEhKC5s2bY/r06WjSpAmsrKxw4sQJXLp0Cc8+++wjHz9x4kQMGDAAzz77LMLCwrB9+3Zs2bIFe/fuBQA4Ojriueeew7x58+Dr64vbt29j6tSpJT7X+++/Dw8PD3h5eeHdd9+Fp6cnevXqVeJ9MzMzER4ejuzsbGzYsAEZGRnIyMgAcL9BVer0SrKbRT8/P5w9exY+Pj56y3/44Qc0bNjQYIURERGR5dBqgR07/r3t4yPw2WdadOhghcdslZWtTp06OHPmDObMmYMpU6bgzz//hL29PRo2bIgJEyYgKirqkY/v3bs3PvroIyxcuBCjR4+Gn58f1qxZg3bt2unus3r1akRERKB58+aoV68eFixYgPDw8GLPNW/ePIwZMwa//vormjZtim+//RZ2dnYl5p46dQrHjx8HcH9z+oOuXbsGX19feW9EGcluFidOnIhRo0bh3r17EEIgPj4eX375JebOnYuVK1cqUSMRET0gLi4OAQEBulOXEZkDKyvg/7feIioKmDcPKOXMNgbh7e2NpUuXYunSpcXWCSGg1Wp1v5ckKirqkU1lgwYNcPTo0WLP+7A2bdrg/PnzJWY/rF27dqXWoyTZzeLw4cNRUFCASZMmITs7G4MHD0b16tXx0Ucf4aWXXlKiRiIiwv1NUJMmTUJsbCw6duyIXbt2qV0SkUF98cW/V3AR4v5sI6mvXGdwfO211/Daa6/h9u3b0Gq1qFKliqHrIqKngJWVFdq2bYvCwkLd0X+kjH379mHEiBG605XduXMHWq3WIi4BZ8ksbYy5uyt/qT+S74lO9+3p6WmoOojoKeTo6Ii4uDhkZWWVeLoJenIPziY+KDw83CKaB0vHMWa+1NqkXB6ym8WgoKASz/8jSRIcHBzg7++PYcOGyT6DORER6YuPj8eAAQN0s4lERGqQ/Wdp586dcfXqVTg7OyM0NBTt2rWDi4sLfv/9d7Ro0QIpKSno0KEDr+ZCRPSEoqOj2SiSyXtaZscslSH+/8ieWbx9+zbeeustvPfee3rLZ82ahaSkJOzevRvR0dGYOXNmqecJIiLzoNFo4OvrCyEE/vjjDx6da2AzZsxAYmIiG0YLZspjrOiqIdnZ2dxEbsKKrv7zJFd5kd0sbtq0CadOnSq2/KWXXkKzZs3w2WefYdCgQVi8eHG5iyKip8ft27fVLsFstWzZEgkJCSXus0iWw1THmLW1Ndzd3XHr1i0AgJOT02MvUydH0SlkrKysDPq8lpIthEB2djZu3boFd3f3JzoYTnaz6ODggCNHjhQ7GeSRI0d0l5zRarWwt7cvd1FERHSfq6srYmJi0K9fP72joXft2oVZs2bxaGhSVdWqVQFA1zAaWlHTpAZzyXZ3d9f9fyov2c3im2++icjISJw6dQotWrSAJEmIj4/HypUr8c477wC4/yVW0iUBiYiofMLCwvRmGT09PXk0NKlOkiR4e3ujSpUqyM/PN+hzF82MGXrG0pKybW1tDfIHpexmcerUqfDz88OyZcvw+eefAwDq1auHzz77DIMHDwYAREZGPvZSOUREJE/RLGP//v0REBCgdjlEOtbW1gaf5RZCoKCgAA4ODqo0bJaYXZpynWfx5Zdfxssvv1zqeu7oSkSknNDQUGRlZaldBhFZCG7DICIiIqJSyZ5ZLCwsxIcffohNmzYhOTkZeXl5euvT0tIMVhwRmTYrKys0b97cYi5FRmRsHGNkCmR/8mbMmIHFixdjwIABSE9Px/jx49GnTx9YWVlh+vTpsp5r7ty5aNGiBVxdXVGlShX07t0bly9ffuRj9u/fD0mSiv1cunRJ7kshoifk6OiI+Ph4HDhwgLufECmAY4xMgexm8X//+x8+++wzTJgwATY2Nhg0aBBWrlyJadOm4dixY7Ke68CBAxg1ahSOHTuGPXv2oKCgAOHh4dBoNI997OXLl5GSkqL7qVu3rtyXQkRERESPIXszdGpqKgIDAwEALi4uSE9PBwB079692FVdHufHH3/Uu71mzRpUqVIFp06dwgsvvPDIx1apUgXu7u6y8oiIiIhIHtnNYo0aNZCSkoJatWrB398fu3fvxrPPPosTJ0488Ym4ixrPSpUqPfa+QUFBuHfvHho2bIipU6ciNDS0xPvl5uYiNzdXdzsjIwPA/UPTlb6eZdHzq3HdTGYz2xiys7PRqFEjCCFw4cIFODs7GzXfEt9zZltWNscYs42R9TiSkFnN5MmT4ebmhnfeeQfffPMNBg0aBF9fXyQnJ2PcuHGYN29euQvu1asX/vnnH/z888+l3u/y5cs4ePAgmjVrhtzcXHz++eeIjY3F/v37S5yNnD59OmbMmFFs+Z9//gk3N7dy1UpE92k0Gnh7ewMAUlJSjP4PGZG54xgjJWVkZKBGjRpIT09/ZE8ku1l82PHjx3H48GH4+/ujZ8+e5X6eUaNGYceOHTh06BBq1Kgh67E9evSAJEn47rvviq0raWaxZs2auHv3ruLNohACGo0Gzs7OqpzUk9nMVppGo4GrqyuA+2PLxcXFaNmAZb7nzLasbI4xZispIyMD7u7uj20WZW+GPnjwIFq3bg0bm/sPbdWqFVq1aoWCggIcPHjwsfsaluTNN9/Ed999h4MHD8puFAHgueeew4YNG0pcZ29vX+Lm8aKjqI3BmFnMZrYxsx/MsqTXzWxmGzNPreyH62C2+WWX9fllHw0dGhpa4rkU09PTS91vsDRCCLzxxhvYsmULfvrpJ/j5+cktBwBw5swZ3TQ9ERERERmO7JlFIUSJneidO3dk70sxatQofPHFF/j222/h6uqK1NRUAECFChV055OaMmUKbty4gfXr1wMAlixZAl9fXzRq1Ah5eXnYsGEDNm/ejM2bN8t9KURERET0GGVuFvv06QPg/pTlsGHD9DbtFhYW4ty5c2jdurWs8JiYGABAu3bt9JavWbMGw4YNA3B/h97k5GTdury8PEyYMAE3btyAo6MjGjVqhB07dqBr166ysomIiIjo8crcLFaoUAHA/ZlFV1dXvTPJ29nZ4bnnnsNrr70mK7wsx9asXbtW7/akSZMwadIkWTlEpAxJktCwYUNotVrV9ushMmccY2QKytwsrlmzBgDg6+uLCRMm8PB9IoKTkxPOnz+PrKwsODk5qV0OkdnhGCNTIHufxejoaCXqICIiIiITJPto6Js3b2LIkCGoVq0abGxsYG1trfdDREREROZD9szisGHDkJycjPfeew/e3t7ch4LIgmVnZ6NFixbQarU4efIkd08hMjCOMTIFspvFQ4cO4eeff8YzzzyjQDlE9DQRQuDixYu634nIsDjGyBTI3gxds2ZNfmCJiIiILITsZnHJkiWYPHky/vjjDwXKISIiIiJTInsz9MCBA5GdnY06derAyckJtra2eutLuhQgERERET2dZDeLS5YsUaAMIiIiIjJFspvFoUOHKlEHEREREZkg2fssAsDvv/+OqVOnYtCgQbh16xYA4Mcff8SFCxcMWhwRmTZJkuDj44NatWrxNFpECuAYI1Mgu1k8cOAAAgMDcfz4cWzZsgVZWVkAgHPnzvHqLkQWxsnJCdeuXcP58+d5KTIiBXCMkSmQ3SxOnjwZs2bNwp49e2BnZ6dbHhoaiqNHjxq0OCIiIiJSl+xmMSEhAS+++GKx5ZUrV8adO3cMUhQRERERmQbZzaK7uztSUlKKLT9z5gyqV69ukKKI6OmQk5ODli1bom3btsjJyVG7HCKzwzFGpkD20dCDBw/G22+/ja+//hqSJEGr1eLw4cOYMGECXnnlFSVqJCITVXS92qLficiwOMbIFMieWZw9ezZq1aqF6tWrIysrCw0bNsQLL7yA1q1bY+rUqUrUSEREREQqkT2zaGtri//973+YOXMmTp8+Da1Wi6CgINStW1eJ+oiIiIhIRbKbxSK1a9dG7dq1DVkLEREREZkY2Zuh+/Xrh3nz5hVbvnDhQvTv398gRRERERGRaSjXSbm7detWbHnnzp1x8OBBgxRFRERERKZB9mborKwsvZNxF7G1tUVGRoZBiiKip4enpyeEEGqXQWS2OMZIbbJnFhs3boyvvvqq2PKNGzeiYcOGBimKiJ4Ozs7OuHXrFq5duwZnZ2e1yyEyOxxjZApkzyy+99576Nu3L37//Xe0b98eALBv3z58+eWX+Prrrw1eIBERERGpR3az2LNnT2zbtg1z5szBN998A0dHRzRp0gR79+5F27ZtlaiRiIiIiFQiq1ksKCjA7NmzERERgcOHDytVExE9JXJyctClSxcUFhZi165dcHJyUrskIrPCMUamQFazaGNjg4ULF2Lo0KFK1UNETxGtVosDBw7oficiw+IYI1Mg+wCXDh06YP/+/QqUQkRERESmRvY+i126dMGUKVNw/vx5NGvWrNjRWT179jRYcURERESkLtnNYlRUFABg8eLFxdZJkoTCwsInr4qIiIiITILsZpH7TBARERFZDtn7LD7o3r17hqqDiIiIiEyQ7GaxsLAQM2fORPXq1eHi4oKrV68CuH+y7lWrVhm8QCIybU5OTjydB5GCOMZIbbKbxdmzZ2Pt2rVYsGCB3jWiAwMDsXLlSoMWR0SmzdnZGVlZWUhNTeWlyIgUwDFGpkB2s7h+/XqsWLECL7/8MqytrXXLmzRpgkuXLhm0OCIiIiJSl+xm8caNG/D39y+2XKvVIj8/3yBFEREREZFpkN0sNmrUCD///HOx5V9//TWCgoIMUhQRPR3u3buH7t27o1+/fjzgjUgBHGNkCmSfOic6OhpDhgzBjRs3oNVqsWXLFly+fBnr16/H999/r0SNRGSiCgsLsXPnTt3vRGRYHGNkCmTPLPbo0QNfffUVdu7cCUmSMG3aNCQmJmL79u3o2LGjEjUSERGRSu7eBeLi1K6C1CR7ZhEAOnXqhE6dOhm6FiIiIjIxI0Y4Ys8eCZGRwIIFgKur2hWRsZWrWQSAkydPIjExEZIkoUGDBmjWrJkh6yIiIiKVabXAnTsSACA2FvjhB2D1aqB9e5ULI6OS3Sz++eefGDRoEA4fPgx3d3cAwN27d9G6dWt8+eWXqFmzpqFrJCIiIhVYWQHh4QU4ffr+qfKSkoCwMHCW0cLI3mcxIiIC+fn5SExMRFpaGtLS0pCYmAghBEaMGKFEjURERGRCYmOBwEAgPl7tSsgYZM8s/vzzzzhy5Ajq1aunW1avXj0sXboUISEhBi2OiIiITFNSEhAdfX/TNJk32c1irVq1Sjz5dkFBAapXr26Qoojo6eDs7AytVousrCxeioxIAaY8xnx8gBkz1K6CjEH2ZugFCxbgzTffxMmTJyGEAHD/YJcxY8Zg0aJFBi+QiIiITEtUFJCQALRsqXYlZAyyZxaHDRuG7OxstGrVCjY29x9eUFAAGxsbREREICIiQnfftLQ0w1VKRERERqXVArt2/dsq+PjwaGhLJLtZXLJkiQJlENHT6N69exgyZAgKCgrwxRdfwNHRUe2SiMyK2mPMygrw9Ly/FTEqCpg/n0dAWyLZzeLQoUOVqIOInkKFhYX45ptvdL8TkWGZwhhbtSoHv/7qgvbtJVXySX2y91kkIiIiy+HuDoSGql0FqYnNIhERERGVis0iEVE5xMXFIT09Xe0yiIgUp2qzOHfuXLRo0QKurq6oUqUKevfujcuXLz/2cQcOHECzZs3g4OCA2rVrIzY21gjVEhEBmZmZiIqKQlhYmN7ZH4iIzJWqzeKBAwcwatQoHDt2DHv27EFBQQHCw8Oh0WhKfcy1a9fQtWtXPP/88zhz5gzeeecdjB49Gps3bzZi5URkifbt24fAwEDdH6h37tyBVqtVuSoiImXJPhpao9Fg3rx52LdvH27dulXsi/Lq1atlfq4ff/xR7/aaNWtQpUoVnDp1Ci+88EKJj4mNjUWtWrV0p/Bp0KABTp48iUWLFqFv377yXgwRURlkZmZi0qRJxbZihIeHw8qKe/MQkXmT3Sy++uqrOHDgAIYMGQJvb29IkuEOpS/a/6dSpUql3ufo0aMIDw/XW9apUyesWrUK+fn5sLW11VuXm5uL3Nxc3e2MjAwAgBBCdwUapRQ9v9I5zGa2WtmOjo7IyMiARqOBo6Oj0fON8brj4+MxcOBAJCUlPbIGY7LEz5qlZlvCGGO2etllzZDdLP7www/YsWMHQkJCZBf1KEIIjB8/Hm3atEHjxo1LvV9qaiq8vLz0lnl5eaGgoAC3b9+Gt7e33rq5c+diRgkXr8zKyjLajMCjNqszm9nmkO3s7Izs7GxVsgFlX/fUqVNLbRSVzn4cZltOtjmPMWarl52VlVWm+8luFitWrPjImb/yeuONN3Du3DkcOnTosfd9eDazqDMuaZZzypQpGD9+vO52RkYGatasCRcXF7i4uDxh1Y8mhIBGo4Gzs7NBZ2CZzWxmGy971qxZ+PXXX0ttGM31dTOb2cw2/+yy7nMtu1mcOXMmpk2bhnXr1sHJyUl2YSV588038d133+HgwYOoUaPGI+9btWpVpKam6i27desWbGxs4OHhUez+9vb2sLe3L7ZckiSjfQCMmcVsZhszOzc3F6+//jry8/OxatUqODg4GC37QUq+7latWiEhIaHEfRaVzn4cZpt/tiWMMWarl13W55e9HfaDDz7Arl274OXlhcDAQDz77LN6P3IIIfDGG29gy5Yt+Omnn+Dn5/fYxwQHB2PPnj16y3bv3o3mzZsX21+RiJRVUFCAdevW4YsvvkBBQYHa5SjG1dUVMTEx2Lt3L3x8fHTLd+3axaOhSVGWMsbItMmeWezdu7fBwkeNGoUvvvgC3377LVxdXXUzhhUqVNBdLH3KlCm4ceMG1q9fDwCIjIzEsmXLMH78eLz22ms4evQoVq1ahS+//NJgdRERlSQsLExvltHT05NHQxOR2ZPdLEZHRxssPCYmBgDQrl07veVr1qzBsGHDAAApKSlITk7WrfPz88POnTsxbtw4LF++HNWqVcPHH3/M0+YQkVEUzTL2798fAQEBapdDRKQ42c2iIZXlkO21a9cWW9a2bVucPn1agYqIiMomNDS0zEcSEhE9zcrULFaqVAlXrlyBp6cnKlas+MgdItPS0gxWHBERERGpq0zN4ocffghXV1cA0F05hYiIiIjMX5maxaFDh5b4OxERERGZN1X3WSSip5uTkxNu3rwJjUZjsPOuEtG/OMbIFLBZJKJykyQJlStXhqOjo2onriUyZxxjZAp4gjAiIiIiKhWbRSIqt9zcXIwaNQrjx49Hbm6u2uUQmR2OMTIFbBaJqNwKCgoQExODlStX8lJkRArgGCNTYNBmMSIiAp9//rkhn5KIiIiIVGTQZvHq1auYNm0amjZtasinJSIiIiKVGPRo6P379wMALl++bMinJSIiIiKVKLLPYr169ZR4WiIiIiIyMtnN4rp167Bjxw7d7UmTJsHd3R2tW7dGUlKSQYsjIiIiInXJbhbnzJkDR0dHAMDRo0exbNkyLFiwAJ6enhg3bpzBCyQiIiIi9cjeZ/H69evw9/cHAGzbtg39+vXDf//7X4SEhKBdu3aGro+ITJijoyOuXr0KjUaj+yOSiAyHY4xMgeyZRRcXF9y5cwcAsHv3bnTo0AEA4ODggJycHMNWR0QmzcrKCr6+vvDx8YGVFU/bSmRoHGNkCmTPLHbs2BGvvvoqgoKCcOXKFXTr1g0AcOHCBfj6+hq6PiIiIiJSkew/U5YvX47WrVvj77//xubNm+Hh4QEAOHXqFAYNGmTwAonIdOXl5WHixImYOnUq8vLy1C6HyOxwjJEpkIQQoqx3LigowOzZsxEREYGaNWsqWZdiMjIyUKFCBaSnp8PNzU3RLCEEsrKy4OLiAkmSFM1iNrPVyNZoNHBxcQEAZGZm6n43Fkt8z5ltWdkcY8xWUll7IlkzizY2Nli4cCEKCwufuEAiIiIiMn2yN0N36NBBd6UWIiIiIjJvsg9w6dKlC6ZMmYLz58+jWbNmcHZ21lvfs2dPgxVHREREROqS3SxGRUUBABYvXlxsnSRJ3ERNREREZEZkN4tarVaJOoiIiIjIBD3RGT7v3btnqDqIiIiIyATJbhYLCwsxc+ZMVK9eHS4uLrh69SoA4L333sOqVasMXiARmS5HR0ckJCTg+PHjvBQZkQI4xsgUyG4WZ8+ejbVr12LBggWws7PTLQ8MDMTKlSsNWhwRmTYrKys0atQIDRo04KXIiBRg6WPs7l0gLk7tKkj2J2/9+vVYsWIFXn75ZVhbW+uWN2nSBJcuXTJocURERGS5RoxwRFiYhKgoIDNT7Wosl+xm8caNG/D39y+2XKvVIj8/3yBFEdHTIS8vD9OnT8ecOXN4KTIiBVjyGNNqgTt37l/BJDYWCAwEfvpJ5aIslOxmsVGjRvj555+LLf/6668RFBRkkKKI6OmQn5+P999/H/PmzeMfi0QKsOQxZmUFhIcX6G4nJQFhYeAsowpknzonOjoaQ4YMwY0bN6DVarFlyxZcvnwZ69evx/fff69EjUREREQA7s8y/vADsGkT0LKl2tVYBtkziz169MBXX32FnTt3QpIkTJs2DYmJidi+fTs6duyoRI1EREREOklJQHS02lVYDtkziwDQqVMndOrUydC1EBERET2Wjw8wY4baVViOcjWLAHDy5EkkJiZCkiQ0aNAAzZo1M2RdRERERMVERQHz5wOurmpXYjlkN4t//vknBg0ahMOHD8Pd3R0AcPfuXbRu3RpffvklatasaegaiYiIyMJotcCuXf+2KT4+wOrVQPv2KhZloWTvsxgREYH8/HwkJiYiLS0NaWlpSExMhBACI0aMUKJGIiIisjBWVoCnpwBwfzYxIYGNolpkzyz+/PPPOHLkCOrVq6dbVq9ePSxduhQhISEGLY6ITJuDgwOOHz+O7OxsODg4qF0Okdmx9DG2alUOfv3VBe3bS2qXYtFkN4u1atUq8VxPBQUFqF69ukGKIqKng7W1NVq0aIGsrCy9KzoRkWFY+hhzdwdCQ9WugmRvhl6wYAHefPNNnDx5EkLcnx4+efIkxowZg0WLFhm8QCIiIiJSj+yZxWHDhiE7OxutWrWCjc39hxcUFMDGxgYRERGIiIjQ3TctLc1wlRKRycnLy8OSJUuQl5eHiRMnwt7eXu2SiMwKxxiZAtnN4pIlSxQog4ieRvn5+Xj77bcBAGPHjuU/ZEQGxjFGpkB2szh06FAl6iAiIiIiE1Tuk3IDQE5OTrGDXdzc3J6oICIiIiIyHbIPcNFoNHjjjTdQpUoVuLi4oGLFino/RERERGQ+ZDeLkyZNwk8//YRPPvkE9vb2WLlyJWbMmIFq1aph/fr1StRIRERERCqRvRl6+/btWL9+Pdq1a4eIiAg8//zz8Pf3h4+PD/73v//h5ZdfVqJOIiIiIlKB7JnFtLQ0+Pn5Abi/f2LR6XHatGmDgwcPGrY6IiIiIlKV7JnF2rVr448//oCPjw8aNmyITZs2oWXLlti+fTvc3d0VKJGITJWDgwN++ukn5OTkWOSlyIiUxjFGpkB2szh8+HD88ssvaNu2LaZMmYJu3bph6dKlKCgowOLFi5WokYhMlLW1Ndq1a2exlyIjUhrHGJkC2c3iuHHjdL+HhoYiMTERp06dQp06ddC0aVODFkdERERE6nqi8ywCgI+PD3x8fAxRCxE9ZfLz8/Hpp58iNzcXb775Juzs7NQuiciscIyRKZB9gAsA7Nu3D927d0edOnXg7++P7t27Y+/evYaujYhMXF5eHt58801MmDABeXl5apdDpJi4uDikp6cbPZdjjEyB7GZx2bJl6Ny5M1xdXTFmzBiMHj0abm5u6Nq1K5YtWybruQ4ePIgePXqgWrVqkCQJ27Zte+T99+/fD0mSiv1cunRJ7ssgIiJ6rMzMTERFRSEsLAwRERFql0OkCtmboefOnYsPP/wQb7zxhm7Z6NGjERISgtmzZ+stfxyNRoOmTZti+PDh6Nu3b5kfd/nyZb3LClauXLnMjyUiIiqLffv2YcSIEUhKSgIA3LlzB1qtlgeakMWR3SxmZGSgc+fOxZaHh4fj7bfflvVcXbp0QZcuXeSWgCpVqvA0PUREpIjMzExMmjQJsbGxesvDw8NhZVWuvbeInmqym8WePXti69atmDhxot7yb7/9Fj169DBYYY8SFBSEe/fuoWHDhpg6dSpCQ0NLvW9ubi5yc3N1tzMyMgAAQggIIRSts+j5lc5hNrPVyn4wzxhjqrR8S3rPma1sdnx8PAYOHKibTSytBmPhGGO2MbIeR3az2KBBA8yePRv79+9HcHAwAODYsWM4fPgw3nrrLXz88ce6+44ePVru0z+St7c3VqxYgWbNmiE3Nxeff/45wsLCsH//frzwwgslPmbu3LmYMWNGseVZWVlG+wtRo9EYJYfZzDZ29oN5lvS6mW2+2VOnTi21UVQ6+3F55vqeM1u97KysrDLdTxIyW9eiS/099oklCVevXi3z80qShK1bt6J3795yykGPHj0gSRK+++67EteXNLNYs2ZN3L17V2+/RyUIIaDRaODs7AxJkhTNYjaz1cjWaDRwdXUFcH9subi4GC0bsMz3nNnKZj9qZnHy5MmYPXs2xxizzSY7IyMD7u7uSE9Pf2RPJHtm8dq1a09UmKE999xz2LBhQ6nr7e3tYW9vX2x50ZHUxmDMLGYz25jZDg4O2L59u+5SZJbyupltvtmtWrVCQkJCifssKp1dEo4xZiudURZP/Z66Z86cgbe3t9plEFkkGxsbdOvWDZ07d4aNzROf45/IJLi6uiImJgZ79+7Vu+jErl27oNVqjVoLxxiZAlU/eVlZWfjtt990t69du4azZ8+iUqVKqFWrFqZMmYIbN25g/fr1AIAlS5bA19cXjRo1Ql5eHjZs2IDNmzdj8+bNar0EIiIyU2FhYXqzjJ6enjwamiySqs3iyZMn9Y5kHj9+PABg6NChWLt2LVJSUpCcnKxbn5eXhwkTJuDGjRtwdHREo0aNsGPHDnTt2tXotRPR/UuRbdiwAbm5uYiIiOClyMjsFM0y9u/fHwEBAUbP5xgjUyD7AJenXUZGBipUqPDYnTkNQQiBrKwsuLi4qLKDLLOZrTSNRqPb4T4zM1OVne8t7T1ntmVlc4wxW0ll7Yk4n05EREREpSrTZuhz586V+QmbNGlS7mKIiIiIyLSUqVl85plnIElSqWf6LlonSRIKCwsNWiARERERqadMzaKpnVuRiIiIiIyjTM3ig+eZIiIiIiLLUe5T51y8eBHJycnIy8vTW96zZ88nLoqIiIiITIPsZvHq1at48cUXkZCQoLcfY9Hh3dxnkchy2Nvb46uvvsK9e/dKvKwmET0ZjjEyBbJPnTNmzBj4+fnh5s2bcHJywoULF3Dw4EE0b94c+/fvV6BEIjJVNjY26N+/P1588UVeioxIARxjZApkf/KOHj2Kn376CZUrV4aVlRWsrKzQpk0bzJ07F6NHj8aZM2eUqJOIiIiIVCB7ZrGwsFB3BnlPT0/89ddfAO4fBHP58mXDVkdEJq2goABff/01tm7dioKCArXLITI7HGNkCmTPLDZu3Bjnzp1D7dq10apVKyxYsAB2dnZYsWIFateurUSNRGSicnNzMXDgQABAnz59YGtrq3JFROaFY4xMgexmcerUqdBoNACAWbNmoXv37nj++efh4eGBr776yuAFEhEREZF6ZDeLnTp10v1eu3ZtXLx4EWlpaahYsaLRL7ZNRERERMqSvc9iSSpVqsRGkYiIFBUXF4f09HS1yyBSVFxcHJKTMxAXp3Yl/5LdLGo0Grz33nto3bo1/P39Ubt2bb0fIiIiQ8rMzERUVBTCwsIQERGhdjlEinjwc9627Z8IC5MQFQVkZqpdWTk2Q7/66qs4cOAAhgwZAm9vb84oEhGRYvbt24cRI0YgKSkJAHDnzh1otVpYW1urXBmR4eh/ziXk5Nw/60xsLPDDD8Dq1UD79urVJ7tZ/OGHH7Bjxw6EhIQoUQ8REREyMzMxadIkxMbG6i0PDw+HlZVB9qAiUl3Jn3OBoKAUHD7cCACQlASEhQGRkcCCBYCrq/HrlN0sVqxYEZUqVVKiFiJ6ytjZ2WH16tXIzc2FnZ2d2uWQmYiPj8eAAQN0s4mWjGPMfMn9nBfNMm7aBLRsqXBxD5HdLM6cORPTpk3DunXr4OTkpERNRPSUsLW1xbBhw5CVlcXzv5HBREdHs1H8fxxj5qs8n/OkJCA6+n7TaEyym8UPPvgAv//+O7y8vODr61vsw3v69GmDFUdERJZnxowZSExMZMNIZq08n3MfH2DGDAWLKoXsZrF3794KlEFET6OCggL8+OOPyMnJQe/evTnzQQbRsmVLJCQklLjPoqXhGDNfcj/nUVHA/Pnq7LMoCSGE8WPVk5GRgQoVKiA9PR1ubm6KZgkhkJWVBRcXF6MfNc5sZhuDRqPRXSs+MzNT97uxWOJ7bmnZDx8NHRQUhBMnThj9aGiOMfP/rKmZ/fDR0I6OCcjJuX+Ai4+PckdDl7Un4iFlRERkssLCwpCQkIDIyEgAgKenJ4+GJrOj/zkXcHLKAnB/NjEhQd3T5gBl3AxdqVIlXLlyBZ6eno+9rF9aWprBiiMiInJ1dUVMTAz69++PgIAAtcshUsSDn3Mvrxq4eVOgfXvTOJd1mZrFDz/8EK7/v5F8yZIlStZDRERUotDQUGRlZaldBpGiij7nDRuqXcm/ytQsDh06tMTfiYiIiMi8yT4aOiMjo8TlkiTB3t6eJw0lIqOJi4tDQECA0Xf6JyKyJLL3EnZ3d0fFihWL/bi7u8PR0RE+Pj6Ijo6GVqtVol4iImRmZiIqKgphYWGIiIhQuxwiIrMme2Zx7dq1ePfddzFs2DC0bNkSQgicOHEC69atw9SpU/H3339j0aJFsLe3xzvvvKNEzURkIuzs7LB06VKjXors4VOp3LlzB1qt1uinUiEyBjXGGNHDZDeL69atwwcffIABAwbolvXs2ROBgYH49NNPsW/fPtSqVQuzZ89ms0hk5mxtbTFq1CijXIosMzOzxJPXhoeH81QqZLaMOcaISiP7G/bo0aMICgoqtjwoKAhHjx4FALRp0wbJyclPXh0REYD4+HgEBgZa/NU8iIjUILtZrFGjBlatWlVs+apVq1CzZk0A9zcLVaxY8cmrIyKTVlhYiP379+Pnn39GYWGhYjnR0dG8TjBZJGONMaJHkb0ZetGiRejfvz9++OEHtGjRApIk4cSJE7h06RK++eYbAMCJEycwcOBAgxdLRKbl3r17aP//lxZQ8lJkM2bMQGJiIhtGsjjGGmNEjyJ7ZrFnz564fPkyunTpgrS0NNy+fRtdunTBpUuX0L17dwBAVFQUFi9ebPBiicgytWzZUu+Sb0REZDyyZxYBwNfXF/PmzTN0LUREpSq6FFa/fv30jobetWsXZs2axaOhiYgUUqZm8dy5c2jcuDGsrKxw7ty5R963SZMmBimMiKgkYWFhSEhI0B0Z7enpyaOhiYgUVKZm8ZlnnkFqaiqqVKmCZ555BpIkQQhR7H6SJHEHXCJSXNEsY//+/REQEKB2OUREZq1MzeK1a9dQuXJl3e9ERKYgNDQUWVlZapdBRGTWytQs+vj4AADy8/Mxffp0vPfee6hdu7aihRERERGR+mQd4GJra4utW7fivffeU6oeInqK2NraYv78+cjLy+PVJYgUYMljLC4uDgEBATxdkAmQvVf4iy++iG3btilQChE9bezs7DBx4kSMGTOG160lUoAljrHMzExERUUhLCwMERERapdDKMepc/z9/TFz5kwcOXIEzZo1g7Ozs9760aNHG6w4IiIishz79u3TOzXWnTt3oNVqeWoslcluFleuXAl3d3ecOnUKp06d0lsnSRKbRSILUlhYiFOnTiE7Oxtt2rSBjU25Tt1KRKWwlDGWmZmpOx3Wg8LDw3lqLBMg+1PHo6GJqMi9e/fQqlUrALwUGZESLGGMxcfHY8CAAbycpwl7onZdCFHi+RaJiIiIyiI6OpqNookrV7O4fv16BAYGwtHREY6OjmjSpAk+//xzQ9dGREREZm7GjBm6U/SRaZLdLC5evBhRUVHo2rUrNm3ahK+++gqdO3dGZGQkPvzwQyVqJCIiIjPVsmVLJCQkIDIyUu1SqBSym8WlS5ciJiYG8+fPR8+ePdGrVy8sWLAAn3zyCT7++GMlaiQiIiIzVnQJz7179+rNMu7atQtarVbFyggoR7OYkpKC1q1bF1veunVrpKSkGKQoIiIisjxhYWF6s4yenp48GtoEyP4/4O/vj02bNhVb/tVXX6Fu3boGKYqIiIgsU9Es4759+7B69Wq1yyGU49Q5M2bMwMCBA3Hw4EGEhIRAkiQcOnQI+/btK7GJJCLzZWtri2nTplnkpciIjMGSx1hoaCiysrLULoNQjmaxb9++OH78OD788ENs27YNQgg0bNgQ8fHxCAoKUqJGIjJRdnZ2mD59OrKysizmUmRExqT2GLPU6zPHxcXBy6sebt50Qfv2alejvnKdCr5Zs2bYsGGDoWshIiIiE/DgFVU6duyIXbt2qV2SUTz4uj08juHOneqIjAQWLABcXdWuTj2q7jV68OBB9OjRA9WqVYMkSdi2bdtjH3PgwAE0a9YMDg4OqF27drFLAxGR8Wi1Wly4cAGJiYk8YpFIAWqMsX379iEwMFD372vR9ZnNnf7rlpCTc382NTYWCAwEfvpJ3frUpGqzqNFo0LRpUyxbtqxM97927Rq6du2K559/HmfOnME777yD0aNHY/PmzQpXSkQlycnJQWBgIFq1aoWcnBy1yyEyO8YcY5mZmYiKikKHDh30rqhi7tdnLvl1CwQF/XuGl6QkICwMiIoCMjPVqVNNql6RvEuXLujSpUuZ7x8bG4tatWphyZIlAIAGDRrg5MmTWLRoEfr27VviY3Jzc5Gbm6u7nZGRAcA4lyosen41LonIbGYbM7fod7XyLek9Z7ZlZRtrjMXHx2PgwIGlXnbPXN/zx73uh8XGAj/8IPDVV0DLlsrUZMzPWlkzVG0W5Tp69CjCw8P1lnXq1AmrVq1Cfn5+iUeKzZ07FzNmzCi2PCsry2h/KWk0GqPkMJvZxs5+MM+SXjezma1GnpLZU6dOfWTDZK7v+eNed0mSkiRMnVqALVuUnek1xnte1qPNn6hZ/PPPPyFJEqpXr/4kT1Nmqamp8PLy0lvm5eWFgoIC3L59G97e3sUeM2XKFIwfP153OyMjAzVr1oSLi4viR3cJIaDRaODs7AxJkhTNYjaz1ch+MMvZ2dnoR0xa4nvObMvKNtYYmzVrFn799ddSGydzfc8f97pL4uMjMGuWtWL/L4z5WSvrvqiym0WtVotZs2bhgw8+0HWkrq6ueOutt/Duu+8qPlv38BtXNIVa2htqb28Pe3v7Ep/HWB98Y2Yxm9lq/UNmSa+b2cw2Zp4xslu1aoWEhATdkcAl1WGO7/njXvfDoqKA+fMloxwZbYz3vKzPL7uze/fdd7Fs2TLMmzcPZ86cwenTpzFnzhwsXboU7733nuxC5ahatSpSU1P1lt26dQs2Njbw8PBQNJuIiMicWer1mUt+3RJOn/53a6WPD7BvH/DJJ5Z5Ch3ZzeK6deuwcuVKREVFoUmTJmjatClGjhyJzz77DGvXrlWgxH8FBwdjz549est2796N5s2bW9yZ7YmIiJRgqddn1n/dAk5O97eeRkUBCQmw6JNzy94MnZaWhvr16xdbXr9+faSlpcl6rqysLPz222+629euXcPZs2dRqVIl1KpVC1OmTMGNGzewfv16AEBkZCSWLVuG8ePH47XXXsPRo0exatUqfPnll3JfBhEZgK2tLd56661SDzAjoiej1hgrmm3r378/AgICjJartgdft5dXDdy8KdC+vTqb302J7Gax6LyIH3/8sd7yZcuWoWnTprKe6+TJkwgNDdXdLjoQZejQoVi7di1SUlKQnJysW+/n54edO3di3LhxWL58OapVq4aPP/641NPmEJGy7OzssHDhQl7uj0ghao8xS70+c9HrbthQ7UpMg+xmccGCBejWrRv27t2L4OBgSJKEI0eO4Pr169i5c6es52rXrt0jz/FT0mbttm3b4vTp03LLJiIiIqJykL0TQtu2bXHlyhW8+OKLuHv3LtLS0tCnTx9cvnwZzz//vBI1EpGJ0mq1+OOPP5CUlGTWO8ATqYVjjEyB7JnF5ORk1KxZE7Nnzy5xXa1atQxSGBGZvpycHNSuXRvA/UtmGfs8i0TmjmOMTIHsmUU/Pz/8/fffxZbfuXMHfn5+BimKiIiIiEyD7GZRCFHiSRyzsrLg4OBgkKKIiIiIyDSUeTN00ZHKkiThvffeg5OTk25dYWEhjh8/jmeeecbgBRIRERGResrcLJ45cwbA/ZnFhIQEvUP47ezs0LRpU0yYMMHwFRIRERGRasrcLMbFxQEAhg8fjo8++ghubm6KFUVEREREpkH20dBr1qxRog4iIiIiMkGym0UioiI2NjaIiopCfn4+bGz4dUJkaBxjZAr4ySOicrO3t8fy5cuRlZUFe3t7tcshMjscY2QKZJ86h4iIiIgsB5tFIio3IQT+/vtv3L59+5HXeSei8uEYI1Mgu1lct24dduzYobs9adIkuLu7o3Xr1khKSjJocURk2rKzs+Hl5YXatWsjOztb7XKIzA7HGJkC2c3inDlz4OjoCAA4evQoli1bhgULFsDT0xPjxo0zeIFERGQ64uLikJ6ernYZRGREspvF69evw9/fHwCwbds29OvXD//9738xd+5c/PzzzwYvkIiI1JeZmYmoqCiEhYUhIiJC7XKIyIhkN4suLi64c+cOAGD37t3o0KEDAMDBwQE5OTmGrY6IiFS3b98+BAYGIjY2FgBw584daLValasiImORfeqcjh074tVXX0VQUBCuXLmCbt26AQAuXLgAX19fQ9dHREQqyczMxKRJk3RNYpHw8HBYWfH4SCJLIXu0L1++HMHBwfj777+xefNmeHh4AABOnTqFQYMGGbxAIiIyvvj4eL3ZRCKyXLJnFjMyMvDxxx8X+6ty+vTpuH79usEKIyIi9URHR/MMF0QEoBzNop+fH1JSUlClShW95WlpafDz80NhYaHBiiMi02ZjY4OhQ4fyUmRmaMaMGUhMTGTDqDKOMTIFsj95pZ0UNCsrCw4ODk9cEBE9Pezt7bFmzRpeiswMtWzZEgkJCSXus0jGwzFGpqDMzeL48eMBAJIkYdq0aXByctKtKywsxPHjx/HMM88YvEAiIlKHq6srYmJi0K9fP4wYMUI3y7hr1y7MmjUL1tbWKldIRMZQ5mbxzJkzAO7PLCYkJMDOzk63zs7ODk2bNsWECRMMXyERmSwhBDQaDTQaDZydnSFJktolkQLCwsL0Zhk9PT15NLSRcIyRKShzsxgXFwcAGD58OD766CO4ubkpVhQRPR2ys7Ph6uoK4P5pVlxcXFSuiJRSNMvYv39/BAQEqF2OxeAYI1Mge5/FNWvWKFEHERE9BUJDQ5GVlaV2GURkRGVqFvv06YO1a9fCzc0Nffr0eeR9t2zZYpDCiIiIiEh9ZWoWK1SooNtPokKFCooWRERERESmo0zN4oObnrkZmoiIiMhyyD6cLScnB9nZ2brbSUlJWLJkCXbv3m3QwoiIiIhIfbKbxV69emH9+vUAgLt376Jly5b44IMP0KtXL8TExBi8QCIiIiJSj+xm8fTp03j++ecBAN988w2qVq2KpKQkrF+/Hh9//LHBCyQi02VtbY1+/fqhd+/ePEEzkQI4xsgUyD51zoPnfNq9ezf69OkDKysrPPfcc7yGKJGFcXBwwKZNm3i5TyKFcIyRKZA9s+jv749t27bh+vXr2LVrF8LDwwEAt27d4om6iYiIiMyM7GZx2rRpmDBhAnx9fdGqVSsEBwcDuD/LGBQUZPACiYiIiEg9sjdD9+vXD23atEFKSgqaNm2qWx4WFoYXX3zRoMURkWnTaDS6y4/xUmREhscxRqZAdrMIAFWrVkXVqlX1lrVs2dIgBRERERGR6eDl/oiIiIioVLzcHxERERGVipf7IyIiIqJSyT4aesaMGfj999+VqIWIiIiITIzsZnHz5s0ICAjAc889h2XLluHvv/9Woi4iIiIiMgGym8Vz587h3LlzaN++PRYvXozq1auja9eu+OKLL5Cdna1EjURkoqytrdG1a1eEh4fzUmRECuAYI1Mgu1kEgEaNGmHOnDm4evUq4uLi4Ofnh7FjxxY7nQ4RmTcHBwd8//33+Oabb3gpMiIFcIyRKShXs/ggZ2dnODo6ws7ODvn5+YaoiYiIiIhMRLmaxWvXrmH27Nlo2LAhmjdvjtOnT2P69OlITU01dH1EREREpCLZV3AJDg5GfHw8AgMDMXz4cAwePBjVq1dXojYiMnEajQZVqlQBANy8eZOXIiMyMI4xMgWym8XQ0FCsXLkSjRo1UqIeInrK8MA2ImVxjJHaytwsBgcHo3fv3hgyZAgaNGigZE1EREREZCLKvM9iZGQk4uPj0bJlSwQEBGDixIn4+eefIYRQsj4iIiIiUlGZm8WhQ4di8+bNuH37NpYsWYKMjAwMHDgQVapUwbBhw7B161ZOlRMRERGZGdlHQ9vb26Nr16749NNP8ddff+H7779H9erVMW3aNHh6eqJ79+44fPiwErUSERERkZE98XkWW7VqhdmzZyMhIQEJCQkICwtDSkqKIWojIiIiIpXJbhavX7+OP//8U3c7Pj4eY8eOxYoVK1CnTh2MGzcO/fr1K/PzffLJJ/Dz84ODgwOaNWuGn3/+udT77t+/H5IkFfu5dOmS3JdBRAZgZWWFtm3bok2bNrCyeuK/PYnoIRxjZApknzpn8ODB+O9//4shQ4YgNTUVHTp0QOPGjbFhwwakpqZi2rRpZX6ur776CmPHjsUnn3yCkJAQfPrpp+jSpQsuXryIWrVqlfq4y5cvw83NTXe7cuXKcl8GERmAo6Mj4uLikJWVBUdHR7XLITI7HGNkCmQ3i+fPn0fLli0BAJs2bUJgYCAOHz6M3bt3IzIyUlazuHjxYowYMQKvvvoqAGDJkiXYtWsXYmJiMHfu3FIfV6VKFbi7u5cpIzc3F7m5ubrbGRkZAAAhhOJHchc9vxpHjDOb2cxmNrOZzWxmlyXrcWQ3i/n5+bC3twcA7N27Fz179gQA1K9fX9a+inl5eTh16hQmT56stzw8PBxHjhx55GODgoJw7949NGzYEFOnTkVoaGip9507dy5mzJhRbHlWVpbRpvQ1Go1RcpjNbGYzm9nMZjazyyorK6tM95PdLDZq1AixsbHo1q0b9uzZg5kzZwIA/vrrL3h4eJT5eW7fvo3CwkJ4eXnpLffy8ir1GtPe3t5YsWIFmjVrhtzcXHz++ecICwvD/v378cILL5T4mClTpmD8+PG62xkZGahZsyZcXFwUv2ySEAIajQbOzs6QJEnRLGYzW41sjUYDPz8/CCFw7do1o1+KzBLfc2ZbVjbHGLOVpNVqy3Q/2c3i/Pnz8eKLL2LhwoUYOnQomjZtCgD47rvvdJun5Xj4jRBClPrm1KtXD/Xq1dPdDg4OxvXr17Fo0aJSm0V7e3vdTOjDucb6ABgzi9nMNma2JEm4ffu2KtkP18FsZptjNscYs5XOKAvZzWK7du1w+/ZtZGRkoGLFirrl//3vf+Hk5FTm5/H09IS1tXWxWcRbt24Vm218lOeeew4bNmwo8/2JiIiIqOxk77S3YcMGWFtb6zWKAODr64uFCxeW+Xns7OzQrFkz7NmzR2/5nj170Lp16zI/z5kzZ+Dt7V3m+xMRERFR2cmeWXzjjTfg7u6O7t276y0fN24cNm7cKKthHD9+PIYMGYLmzZsjODgYK1asQHJyMiIjIwHc39/wxo0bWL9+PYD7R0v7+vqiUaNGyMvLw4YNG7B582Zs3rxZ7ssgIiIiojKQ3Sxu3LgRL730Er777jvdfoJvvvkmtmzZgri4OFnPNXDgQNy5cwfvv/8+UlJS0LhxY+zcuRM+Pj4AgJSUFCQnJ+vun5eXhwkTJuDGjRtwdHREo0aNsGPHDnTt2lXuyyAiIiKiMpDdLHbu3BmxsbHo3bs3du/ejdWrV+Pbb79FXFwcAgICZBcwcuRIjBw5ssR1a9eu1bs9adIkTJo0SXYGEREREZWP7GYRAF566SX8888/aNOmDSpXrowDBw7A39/f0LURkYmzsrJC8+bNUVhYyEuRESmAY4xMQZmaxQfPU/igKlWqICgoCJ988olu2eLFiw1TGRGZPEdHR8THx/NSZEQK4RgjU1CmZvHMmTMlLq9Tpw4yMjJ069U6FxERERERKaNMzaLcA1eIiIiIyDzI3gEiPT0daWlpxZanpaUhIyPDIEUR0dMhOzsbfn5+aNy4MbKzs9Uuh8jscIyRKZDdLL700kvYuHFjseWbNm3CSy+9ZJCiiOjpIIRAUlISkpOTIYRQuxwis8MxRqZAdrN4/PhxhIaGFlverl07HD9+3CBFEREREZFpkN0s5ubmoqCgoNjy/Px85OTkGKQoIiIiIjINspvFFi1aYMWKFcWWx8bGolmzZgYpioiIiIhMg+yTcs+ePRsdOnTAL7/8grCwMADAvn37cOLECezevdvgBRIRERGRemTPLIaEhODo0aOoWbMmNm3ahO3bt8Pf3x/nzp3D888/r0SNRERERKSScl3u75lnnsH//vc/Q9dCRE8ZSZLQsGFDaLVanpSfSAEcY2QKytQsZmRkwM3NTff7oxTdj4jMn5OTE86fP4+srCw4OTmpXQ6R2eEYI1NQpmaxYsWKSElJQZUqVeDu7l7iXzdCCEiShMLCQoMXSURERETqKFOz+NNPP6FSpUoAeOk/IiIiIktSpmaxbdu2Jf5ORJYtOzsbLVq0gFarxcmTJ+Hs7Kx2SURmhWOMTEGZmsVz586V+QmbNGlS7mKI6OkihMDFixd1vxORYXGMkSkoU7P4zDPPQJKkx35Quc8iERERkXkpU7N47do1pesgIiIiIhNUpmbRx8dH6TqIiIiIyATJvoLL3LlzsXr16mLLV69ejfnz5xukKCIiIiIyDbKbxU8//RT169cvtrxRo0aIjY01SFFEREREZBpkX+4vNTUV3t7exZZXrlwZKSkpBimKiJ4OkiTBx8dHd1J+IjIsjjEyBbKbxZo1a+Lw4cPw8/PTW3748GFUq1bNYIURkelzcnLCtWvXeCkyIoVwjJEpkN0svvrqqxg7dizy8/PRvn17AMC+ffswadIkvPXWWwYvkIiIiIjUI7tZnDRpEtLS0jBy5Ejk5eUBABwcHPD2229jypQpBi+QiIiIiNQju1mUJAnz58/He++9h8TERDg6OqJu3bqwt7dXoj4iMmE5OTl44YUXUFhYiEOHDnEzGZGBcYyRKZDdLBZxcXFBixYtDFkLET1liq5XW/Q7ERkWxxiZAtmnziEiIiIiy8FmkYiIiIhKxWaRiIiIiErFZpGIiIiISsVmkYiIiIhKVe6joYmIAMDT0xNCCLXLIDJbHGOkNjaLRFRuzs7OuHXrFrKysuDs7Kx2OURmh2OMTAE3QxMRERFRqdgsEhEREVGp2CwSUbnl5OQgNDQUXbt2RU5OjtrlEJkdjjEyBdxn0UzFxcUhICAALi4uapdiMSzxPddqtThw4IDud0tz9y5w4gTQvr3alZC5svQxRqaBM4tmJjMzE1FRUQgLC0NERITa5VgEvueWa8QIR4SFSYiKAjIz1a6GiEgZbBbNyL59+xAYGIjY2FgAwJ07d/iXqML4nlsurRa4c0cCAMTGAoGBwE8/qVwUEZEC2CyagaKZrQ4dOiApKUm3PDw8HFZW/F+sBL7nZGUFhIcX6G4nJQFhYeAsIxGZHf6r9pSLj4/Xm9ki5fE9p0cpmmWMj1e7EiIiw2Cz+JSLjo7Wm9ki5fE9p8dJSgKio9WugojIMNgsPuVmzJgBHx8ftcuwKHzP9Tk5OcHJyUntMkyKjw8wY4baVZC54BgjtbFZfMq1bNkSCQkJiIyMVLsUi8H3/F/Ozs7IyspCamoqL0X2/6KigIQEoGVLtSshc8AxRqaAzaIZcHV1RUxMDPbu3as347Vr1y4emasQvuek1QK7dv17qlofH2DfPuCTTwBXVxULIyIyMDaLZiQsLExvxsvT05NH5iqM77nlsrICPD0FgH9nE3lybiIyR7yCi5kpmvHq378/AgIC1C7HIljye37v3j307dsXBQUF2LZtGxwdHdUuyahWrcrBr7+6oH17Se1SyExZ+hgj08Bm0UyFhoYiKytL7TIsiiW+54WFhdi5c6fud0vj7g6EhqpdBZkzSx9jZBq4vYyIiIiISsVmUUFxcXFITs5AXJw6+XfvQpVsNV8333PjZ6tNrfdcbWq+bmYTWRihsuXLlwtfX19hb28vnn32WXHw4MFH3n///v3i2WefFfb29sLPz0/ExMTIyktPTxcARHp6+pOU/UgZGRkiMjJSABAeHscEIERkpBAZGYpFFqPVakXHjvlGzVbzdfM9V+d1Z2VlCQACgMjMzDRO6APUeM8fzM7IyBBardZ4oQ9kq/m6mW1ZY0zNzzmzlVXWnkjVZnHjxo3C1tZWfPbZZ+LixYtizJgxwtnZWSQlJZV4/6tXrwonJycxZswYcfHiRfHZZ58JW1tb8c0335Q5U+lmce/evcLHx+f/B7cknJzOC0AIQAgfHyH27VMktpiCAq149tkCo2Wr+br5nqv3utX+h8zY7/mD1PzHRM3XzWzLGmOW0jRZavZT0Sy2bNlSREZG6i2rX7++mDx5con3nzRpkqhfv77estdff10899xzZc5Uqll8cIbnwZ+QkD26L5eiH2P8VarVasXkyfcUz1bzdfM9V/91m8I/ZMZ4z0vLVvMfEzVfN7Mta4xZQtNkqdll7YlUOxo6Ly8Pp06dwuTJk/WWh4eH48iRIyU+5ujRowgPD9db1qlTJ6xatQr5+fmwtbUt9pjc3Fzk5ubqbqenp+v+K4R40pcBADh16hSGDRuG69evF1tXUKABkKG3LDYW2LFDYO1aoFkzg5RQjBACubn5AOwVy1bzdfM9N43XrdFodL+np6cb/WhNY7znj8rWaDQoLCyEJBn31Dlqv25mGy/bFMaYmp9zZiubnZGRoct8XFGquHHjhgAgDh8+rLd89uzZIiAgoMTH1K1bV8yePVtv2eHDhwUA8ddff5X4mOjo6GIzMPzhD3/4wx/+8Ic//Ln/c/369Uf2bKqfZ/HhrlkI8chOuqT7l7S8yJQpUzB+/Hjdba1Wi7S0NHh4eBilY69ZsyauX78ONzc3RbOYzWxmM5vZzGY2s+UQQiAzMxPVqlV75P1UaxY9PT1hbW2N1NRUveW3bt2Cl5dXiY+pWrVqife3sbGBh4dHiY+xt7eHvb3+ZgN3d/fyF14Obm5uRv+wMZvZzGY2s5nNbGY/ToUKFR57H9XOs2hnZ4dmzZphz549esv37NmD1q1bl/iY4ODgYvffvXs3mjdvXuL+ikRERET0ZFQ9Kff48eOxcuVKrF69GomJiRg3bhySk5MRGRkJ4P4m5FdeeUV3/8jISCQlJWH8+PFITEzE6tWrsWrVKkyYMEGtl0BERERk1lTdZ3HgwIG4c+cO3n//faSkpKBx48bYuXMnfHx8AAApKSlITk7W3d/Pzw87d+7EuHHjsHz5clSrVg0ff/wx+vbtq9ZLeCR7e3tER0cX2wzObGYzm9nMZjazmW1q2aWRhDDQ+WOIiIiIyOzw2tBEREREVCo2i0RERERUKjaLRERERFQqNotEREREVCo2i0RERERUKjaLpIj9+/cjJydH7TKMKjc3F7///jtyc3PVLsXobt68WezqSkoqLCzEzZs3cfv2baNlPpx969YtFBYWGj2fyNj4fU5sFg3ol19+waxZs/DJJ58U+0csIyMDERERimWvXLkSQ4cOxZo1awAAX331FRo0aIDatWsjOjpasdzShIeH448//lA048qVK3jwzE+HDh1C79690ahRI3To0AHffvutYtlr167FsWPHAAD37t3Dq6++CmdnZwQEBMDFxQWRkZGKfckEBgZi5syZuH79uiLP/yhpaWno27cvfHx8MGrUKBQWFuLVV1+Ft7c3qlevjtatWyMlJUWx/B07duCFF16As7MzqlWrBi8vL7i7u2PIkCF652RVwtatWxESEgInJydUq1YN3t7ecHJyQkhICLZt26Zo9qMkJiaidu3aij2/mt9rj2LOr5vf5/w+NzmCDGLXrl3Czs5ONGrUSNSqVUt4enqKn376Sbc+NTVVWFlZKZL94YcfCmdnZ9GnTx/h7e0tZs2aJTw8PMSsWbPE+++/LypUqCA+/fRTRbKDgoJK/JEkSTRo0EB3WwlWVlbi5s2bQggh4uLihJWVlejRo4eYPXu26Nu3r7CyshI//vijItn+/v7ixIkTQgghJkyYIHx9fcWWLVtEYmKi2LZtmwgICBATJ05UJFuSJOHh4SGsra1Fp06dxDfffCPy8/MVyXrY8OHDRePGjcXSpUtF27ZtRe/evUWTJk3EoUOHxJEjR0SLFi3EK6+8okj2+vXrhaurqxg7dqyYPHmy8PLyEpMnTxYxMTGibdu2wtPTU1y5ckWR7NjYWGFnZyciIyPF1q1bxZEjR8Thw4fF1q1bRWRkpLC3txcrVqxQJPtxzp49q9h3i5rfa49jrq+b3+f8PjfW97kcbBYNJDg4WLzzzjtCCCG0Wq1YsGCBcHFxET/88IMQQtkvl/r164v//e9/QgghTp8+LWxsbMTKlSt161evXi2aNWumSLaNjY3o3LmzmD59uu4nOjpaWFlZiZEjR+qWKUGSJN2XS1hYmBg5cqTe+smTJ4sXXnhBkWx7e3uRlJQkhBAiICBA9/+5yIEDB0StWrUUyZYkSdy4cUNs3bpV9OjRQ9jY2IjKlSuLt956S1y8eFGRzCLe3t7i8OHDQoj7n2lJksTu3bt16w8dOiSqV6+uSHb9+vXFxo0bdbdPnDghatSoIbRarRBCiIEDB4oXX3xRkew6derojamHrVq1StSuXVuR7HHjxj3y5z//+Y9i3y1qfq9Z6uvm9zm/z431fS4Hm0UDcXNzE7/99pvesi+++EI4OzuL7777TtEvF0dHR90HXYj7H/zz58/rbv/666/C3d1dkexDhw6JOnXqiGnTponCwkLdchsbG3HhwgVFMos8+OXi7e0tjh07prf+woULwsPDQ5FsHx8f3UxD9erVdX+VFrl48aJwdnZWJPvB1y2EECkpKWLOnDmibt26wsrKSgQHB4tVq1Ypku3k5CT++OMP3W1bW1uRkJCgu3316lXFXrejo6O4du2a3jIbGxtx48YNIYQQx48fV+xz7uDgIC5dulTq+sTEROHg4KBItpWVlXj22WdFu3btSvxp3ry5Yt8tan6vWerr5vc5v8+N9X0uB5tFA6lcubI4efJkseUbN24UTk5OIiYmRrEvFw8PD72/QGrUqKH3D/qvv/4qXFxcFMkWQoj09HTx0ksviZYtW+q+YI315fLbb7+J9PR0Ubt2bXHmzBm99b/++qtwcnJSJPudd94RwcHB4p9//hGTJ08WPXr0EJmZmUIIITQajRgwYIAIDw9XJPvBzTUPi4uLE//5z38U+2Jr2rSpWLZsmRBCiJ07dwpXV1fxwQcf6NbHxMSIxo0bK5LdoEED8fXXX+tunzp1StjZ2YmCggIhxP3/30q97mbNmonx48eXun78+PGKzfbUq1dPfP7556WuP3PmjGLfLWp+r1nq6+b3Ob/Piyj9fS4Hm0UD6dixo1i4cGGJ67744gtha2ur2JdLSEiI3ua5h23fvl2xf8AftHr1alG1alXx6aefCltbW6N8uVhZWQkrKyshSVKxzYTbtm0TdevWVSQ7NzdX9OzZU1SsWFF07NhRODg4CCcnJ1G3bl3h7OwsatWqJS5fvqxI9sN/iZYkPT1dkewNGzYIa2tr4e/vLxwcHMQ333wjqlWrJgYMGCBeeuklYWdnp2smDW3ZsmWiQoUKYtKkSWLatGmiWrVqYsSIEXq1KbU/1f79+4Wzs7No2LChGDt2rJg7d66YN2+eGDt2rGjUqJFwcXERBw8eVCR78ODBYuzYsaWuP3v2rJAkSZFsNb/XLPV18/uc3+cPU+r7XA4btQ+wMRdRUVE4ePBgiesGDRoEAFixYoUi2fPnz4ezs3Op65OTk/H6668rkv2g4cOHo02bNnj55ZdRUFCgeF5cXJzebW9vb73bf/zxB1577TVFsu3s7PDtt9/ixx9/xPbt22FtbQ2tVgtvb2+EhIRg8ODBj/x/8iSGDh0KR0fHR97Hzc1NkeyXX34ZPj4+OH78OFq3bo3g4GA0aNAA8+bNQ3Z2NlasWIGhQ4cqkj1q1ChYWVlhw4YNyM3NxbBhw/Dee+/p1rds2RJffPGFItlt27bF+fPnERMTg2PHjulOE1S1alV0794dkZGR8PX1VST7gw8+eOSRmE2bNoVWq1UkW83vNUt93fw+v4/f5/9S6vtcDkmIB45VJzIArVaLzMxMuLm5QZIktcshIqJy4vc5AQBnFhWQlJSE1NRUSJIELy8v+Pj4WGR2hQoVVMu21Pec2WSOLPWzZkrZ/D437+zHUns7uDlZvHixqFGjhm6fi6J9MGrUqCE+/PBDZjOb2U9x9qMoec4/S8621M8as5ltCt9rD2KzaCDvv/++cHNzE/PmzRNnzpwRf/31l7hx44Y4c+aMmDdvnqhQoYKYOXMms5nN7Kcw+3GUPNjCUrMt9bPGbGabyvfag9gsGkiNGjXE1q1bS12/ZcsWUa1aNWYzm9lPYfaLL774yJ/27dsrNsNmqdmW+lljNrONlS0H91k0kDt37qBevXqlrg8ICMA///zDbGYz+ynM3r59Ozp27AgvL68S1xcWFiqSa8nZlvpZYzazjZUti9rdqrlo27atePnll0u8pmN+fr4YPHiwaNu2LbOZzeynMDswMPCRl/tT8gTRlpptqZ81ZjPbWNly8NQ5BpKQkIDw8HDk5uaibdu28PLygiRJSE1NxcGDB2Fvb489e/agUaNGzGY2s5+y7OHDh8PJyQnLly8vcX1iYiK6du2Ka9euMdtALPWzxmxmGytbDjaLBpSZmYkNGzYUO2lvcHAwBg8erOiJNZnNbGYrl52bm4vCwkI4OTkp8vzMLpklftaYzWxjZpcVm0UiIiIiKpWV2gWYs27duiElJYXZzGY2s5nNbGYz2+SzS8NmUUEHDx5ETk4Os5nNbGYzm9nMZrbJZ5eGzSIRERERlYrNooJ8fHxga2vLbGYzm9nMZjazmW3y2aXhAS5EREREVCpewcXAfv31Vxw5cgSpqamQJAleXl5o3bo16taty2xmM5vZzDYQjUaDU6dO4YUXXmA2s5mtNPXOB25e7t69K3r27CkkSRLu7u4iICBA1K1bV7i7uwsrKyvRq1cvkZ6ezmxmM5vZzDaAs2fPKnb1GGYzm9n62CwayJAhQ0RgYKA4duxYsXXHjh0TTZo0Ea+88gqzmc1sZjPbACz1H3BmM1sNbBYNpEKFCiV+oRY5evSoqFChArOZzWxmM7sMKlas+MgfNzc3xf4RZTazLSFbDu6zaECSJJVrHbOZzWxmM1tfbm4uoqKiEBgYWOL6pKQkzJgxg9nMZrYxqN2tmov//Oc/okmTJuLEiRPF1p04cUI888wzYsiQIcxmNrOZzewyaN26tViyZEmp65XcPMdsZltCthxsFg3kn3/+EZ07dxaSJImKFSuKevXqifr164uKFSsKKysr0aVLF/HPP/8wm9nMZjazy2D27Nli+vTppa5PTk4Ww4YNYzazmW0EPM+igV26dAlHjx5FamoqAKBq1aoIDg5G/fr1mc1sZjOb2UT01GGzSERERESl4uX+DEyr1Za6PDk5mdnMZjazmc1sZjPbZLLLgs2igWRkZGDAgAFwdnaGl5cXoqOjUVhYqFv/999/w8/Pj9nMZjazmc1sZjNb9WxZ1N1l0nyMHj1aBAQEiK+//lp89tlnwsfHR3Tr1k3k5uYKIYRITU0VkiQxm9nMZjazmc1sZqueLQebRQOpVauWiIuL092+ffu2aNWqlQgPDxf37t0Tqampih3+zmxmM5vZzGY2s5mtFG6GNpDbt2/Dx8dHd9vDwwN79uxBZmYmunbtiuzsbGYzm9nMZjazmc1sk8iWg82igdSsWROJiYl6y1xdXbF7927k5OTgxRdfZDazmc1sZjOb2cw2iWw52CwaSHh4ONasWVNsuYuLC3bt2gUHBwdmM5vZzGY2s5nNbJPIlkXt7eDmIi0tTZw/f77U9ZmZmWL//v3MZjazmc1sZjOb2apny8GTchMRERFRqWzULsCcaDQafPHFFzhy5AhSU1MhSRK8vLwQEhKCQYMGwdnZmdnMZjazmc1sZjPbJLLLijOLBnLx4kV07NgR2dnZaNu2Lby8vCCEwK1bt3DgwAE4Oztj9+7daNiwIbOZzWxmM5vZzGa2qtmyGHObtzlr166deOmll3Qn0nxQbm6uGDRokGjXrh2zmc1sZjOb2cxmturZcrBZNBBHR0dx4cKFUtcnJCQIR0dHZjOb2cxmNrOZzWzVs+XgqXMMpGLFivj1119LXf/bb7+hYsWKzGY2s5nNbGYzm9mqZ8uidrdqLqKjo0WFChXEwoULxdmzZ0VKSopITU0VZ8+eFQsXLhQVK1YUM2bMYDazmc1sZjOb2cxWPVsONosGNG/ePOHt7S0kSRJWVlbCyspKSJIkvL29xfz585nNbGYzm9nMZjazTSa7rHg0tAKuXbuG1NRUAEDVqlXh5+fHbGYzm9nMZjazmW2S2Y/DZpGIiIiISsUDXAwoJycHhw4dwsWLF4utu3fvHtavX89sZjOb2cxmNrOZbRLZZabuVnDzcfnyZeHj46Pb56Bt27bir7/+0q1PTU0VVlZWzGY2s5nNbGYzm9mqZ8vBmUUDefvttxEYGIhbt27h8uXLcHNzQ0hICJKTk5nNbGYzm9nMZjazTSpbFrW7VXNRpUoVce7cOb1lI0eOFLVq1RK///67on8dMJvZzGY2s5nNbGYrxUbtZtVc5OTkwMZG/+1cvnw5rKys0LZtW3zxxRfMZjazmc1sZjOb2SaRLYva3aq5aNGihVi/fn2J60aNGiXc3d0V++uA2cxmNrOZzWxmM1spbBYNZM6cOaJLly6lro+KihKSJDGb2cxmNrOZzWxmq54tB8+zSERERESl4tHQRERERFQqNotEREREVCo2i0RERERUKjaLRERERFQqNotERAa0f/9+SJKEu3fvql0KEZFB8GhoIqIn0K5dOzzzzDNYsmQJACAvLw9paWnw8vKCJEnqFkdEZAC8ggsRkQHZ2dmhatWqapdBRGQw3AxNRFROw4YNw4EDB/DRRx9BkiRIkoS1a9fqbYZeu3Yt3N3d8f3336NevXpwcnJCv379oNFosG7dOvj6+qJixYp48803UVhYqHvuvLw8TJo0CdWrV4ezszNatWqF/fv3q/NCiciicWaRiKicPvroI1y5cgWNGzfG+++/DwC4cOFCsftlZ2fj448/xsaNG5GZmYk+ffqgT58+cHd3x86dO3H16lX07dsXbdq0wcCBAwEAw4cPxx9//IGNGzeiWrVq2Lp1Kzp37oyEhATUrVvXqK+TiCwbm0UionKqUKEC7Ozs4OTkpNv0fOnSpWL3y8/PR0xMDOrUqQMA6NevHz7//HPcvHkTLi4uaNiwIUJDQxEXF4eBAwfi999/x5dffok///wT1apVAwBMmDABP/74I9asWYM5c+YY70USkcVjs0hEpDAnJyddowgAXl5e8PX1hYuLi96yW7duAQBOnz4NIQQCAgL0nic3NxceHh7GKZqI6P+xWSQiUpitra3ebUmSSlym1WoBAFqtFtbW1jh16hSsra317vdgg0lEZAxsFomInoCdnZ3egSmGEBQUhMLCQty6dQvPP/+8QZ+biEguHg1NRPQEfH19cfz4cfzxxx+4ffu2bnbwSQQEBODll1/GK6+8gi1btuDatWs4ceIE5s+fj507dxqgaiKismOzSET0BCZMmABra2s0bNgQlStXRnJyskGed82aNXjllVfw1ltvoV69eujZsyeOHz+OmjVrGuT5iYjKildwISIiIqJScWaRiIiIiErFZpGIiIiISsVmkYiIiIhKxWaRiIiIiErFZpGIiIiISsVmkYiIiIhKxWaRiIiIiErFZpGIiIiISsVmkYiIiIhKxWaRiIiIiErFZpGIiIiISvV/k8uPpyfNb6IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evolution of clicks relative to originals, figure 4 - first version, not used in the paper\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "\n",
    "labels=[ '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07' ]\n",
    "time=list(range(1,19))\n",
    "x = time\n",
    "y = share.copy()\n",
    "y[6]=None\n",
    "y[13]=None\n",
    "#plt.plot(x, y, marker='o', color='red');\n",
    "#plt.plot(x, y, color='black',linestyle='None', marker='o');\n",
    "plt.scatter(x, y, color='black', marker=8);\n",
    "\n",
    "x = time\n",
    "y = share2.copy()\n",
    "y[6]=None\n",
    "y[13]=None\n",
    "#plt.plot(x, y,marker='o', color='green');\n",
    "#plt.plot(x, y, color='blue', linestyle='', marker='o');\n",
    "plt.scatter(x, y, color='blue', marker=9);\n",
    "plt.ylim(0, 3);\n",
    "plt.xticks(x, labels, rotation='vertical')\n",
    "#plt.axvline(x=7, color='red')\n",
    "#plt.axvline(x=14, color='green')\n",
    "\n",
    "# Pad margins so that markers don't get clip\n",
    "#ax.plot(x, y)\n",
    "ax.set_xlabel('time')\n",
    "ax.set_ylabel('clicks/visits to original papers, in percentages')\n",
    "#ax.set_title('                 Figure 4: the percentage of visits to originals that uses the link to the replications')\n",
    "ax.legend(['Group 1', 'Group 2'])\n",
    "plt.axvline(x=7, color='black', linestyle='--')\n",
    "plt.axvline(x=14, color='black', linestyle='--')\n",
    "\n",
    "plt.grid(axis='x', color='0.95')\n",
    "plt.grid(axis='y', color='0.95')\n",
    "#plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making the more beautiful FIGURE 4 used in the paper\n",
    "import numpy as np\n",
    "from matplotlib import pylab as pl\n",
    "import random\n",
    "import numpy\n",
    "\n",
    "labels=[ '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07' ]\n",
    "X=range(1,19)\n",
    "\n",
    "y1 = share.copy()\n",
    "y1[6]=0\n",
    "y1[13]=0\n",
    "\n",
    "y2 = share2.copy()\n",
    "y2[6]=0\n",
    "y2[13]=0\n",
    "\n",
    "# plot the bars\n",
    "pl.bar(X, y1, color = 'black')\n",
    "pl.bar(X, [ -x for x in y2], color = 'grey')\n",
    "#plt.title(\"Back-to-Back Bar Chart\")\n",
    "plt.axvline(x=7, color='red', linestyle='--')\n",
    "plt.axvline(x=14, color='red', linestyle='--')\n",
    "\n",
    "pl.xticks(X, labels, rotation='vertical')\n",
    "\n",
    "pl.yticks([2,1,0,-1,-2],[2,1,0,1,2])\n",
    "pl.grid(which='major',  linestyle='--')\n",
    "pl.xlabel('Time')\n",
    "pl.ylabel('Clicks / Visits to Original Papers (in Percent)')\n",
    "pl.rc('axes', axisbelow=True)\n",
    "pl.show()\n",
    "\n",
    "#pl.text(-20,175, 'Group 1', fontsize=12, color='black')\n",
    "#pl.text(10,175, 'Group 2', fontsize=12, color='blue')\n",
    "\n",
    "#pl.axvline(0.0)\n",
    "#\n",
    "#pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7f76b64340>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ji38Xk7QoWpKoqCh0794dt2/fhoeHh8nilPU+lbdGYjc0ERERqcpGY4OQgBC10yAD2A1NRERERAaxZZGIiIjIzEJCQoxaeUZNbFkkIiIiIoNYLBIRERGRQSwWiYiIqMJ0Op3aKVAZlHh/eM0iERERGc3e3h4ajQZJSUmoVasW7O3tjVpJhUxLCIG8vDxcv34dGo3G4NyN5cFikYiIiIym0WgQGBiI5ORkJCUlqZ0OGeDs7Ax/f3/9etUVwWKRiIiIKsTe3h7+/v4oKCiAVqtVOx26j42NDWxtbR+6xZfFIhEREVWYJEmws7ODnZ2d2qmQiXCACxEREREZxGKRiIiIiAxisUhEREREBrFYJCIiIiKDWCwSERERkUEsFomIiIjIIBaLRERERGQQi0UiIiIiMojFIhEREREZxGKRiIiIiAxisUhEREREBrFYJCIiIiKDWCwSERERkUEsFomIiIjIIBaLRERERGQQi0UiIiIiMojFIhEREREZpGqxuH//fvTr1w8+Pj6QJAlbtmzR78vPz8ebb76Jli1bwsXFBT4+PhgzZgySkpLUS5iIiIjIyqhaLGZlZaF169ZYsmRJiX3Z2dk4ceIEZs+ejRMnTiAiIgIXLlxA//79VciUiIiIyDrZqhm8T58+6NOnT6n73N3dsWvXrmLbFi9ejEcffRTx8fHw9/c3R4pEREREVk2RYvHOnTvw8PBQ4lRlSktLgyRJZcbKzc1Fbm6u/nF6errJ8yIiIiKqqozuhp4/fz42btyofzx06FB4enqibt26+PPPPxVN7l45OTmYOXMmRowYATc3N4PHzZ07F+7u7vqbn5+fyXIiIiIiquqMLhaXL1+uL8B27dqFXbt24ddff0WfPn3w+uuvK54gIA92GTZsGHQ6Hb744osyj501axbS0tL0t4SEBJPkRERERGQNjO6GTk5O1heLP//8M4YOHYpevXohICAAjz32mOIJ5ufnY+jQoYiNjcWePXvKbFUEAAcHBzg4OCieBxEREZE1MrplsXr16vrWuu3bt6NHjx4AACEEtFqtoskVFooXL17Eb7/9Bk9PT0XPT0RERERlM7plMTQ0FCNGjECjRo1w8+ZN/WjmmJgYNGzY0KhzZWZm4tKlS/rHsbGxiImJQY0aNeDj44MhQ4bgxIkT+Pnnn6HVapGSkgIAqFGjBuzt7Y1NnYiIiIiMZHSx+NlnnyEwMBDx8fH46KOP4OrqCkDunp44caJR5zp27Bi6d++ufzxt2jQAwNixY/H2229j69atAIA2bdoUe97evXsREhJibOpEREREZCSjisX8/Hy8/PLLmD17NurXr19sX3h4uNHBQ0JCIIQwuL+sfURERERkekZds2hnZ4fIyEhT5UJEREREFsboAS6DBg0qtoYzEREREVVdRl+z2LBhQ7z33ns4dOgQ2rVrBxcXl2L7p06dqlhyRERERKQuo4vFr776Ch4eHjh+/DiOHz9ebJ8kSSwWiYiIiKoQo4vF2NhYU+RBRERERBbI6GsWC+Xl5eH8+fMoKChQMh8iIiIisiBGF4vZ2dl44YUX4OzsjKCgIMTHxwOQr1WcN2+e4gkSEREArRaIigLWr5e/KrxiFhGRIUYXi7NmzcKff/6JqKgoODo66rf36NEDGzduVDQ5IiICEBEBBAQA3bsDI0bIXwMC5O1ERCZmdLG4ZcsWLFmyBJ07d4YkSfrtzZs3x+XLlxVNjojI6kVEAEOGANeuFd+emChvZ8FIRCZmdLF4/fp11K5du8T2rKysYsUjERE9JK0WCAsDSlvNqnBbeDi7pInIpIwuFjt06IBffvlF/7iwQPzyyy/RsWNH5TIjIrJ20dElWxTvJQSQkCAfR0RkIkZPnTN37lz07t0bZ86cQUFBARYtWoS///4bv//+O/bt22eKHImIrFNysrLHERFVgNEti506dcLBgweRnZ2NBg0aYOfOnfDy8sLvv/+Odu3amSJHIiLr5O2t7HFERBVgdMsiALRs2RJr1qxROhciIrpXly6Ar688mKW06xYlSd7fpYv5cyMiq1GhYlGr1SIyMhJnz56FJElo1qwZBgwYAFvbCp2OiIhKY2MDLFoEDB5ccl/hgMKFC+XjiIhMxOjq7vTp0xgwYABSUlLQpEkTAMCFCxdQq1YtbN26FS1btlQ8SSIiqxUaCtSrB1y9Wny7r69cKIaGqpIWEVkPo69ZfPHFFxEUFIRr167hxIkTOHHiBBISEtCqVSu8/PLLpsiRiMh6nTwpF4p2dsBHH8nbPDyA2FgWikRkFkYXi3/++Sfmzp2L6tWr67dVr14dH3zwAWJiYpTMjYiIVq6Uvw4aBLz6qnz/zh35RkRkBkYXi02aNME///xTYntqaioaNmyoSFJERAQgJwf47jv5/vjxgKur3CUNAGfPqpcXEVkVo4vFDz/8EFOnTsXmzZtx7do1XLt2DZs3b0Z4eDjmz5+P9PR0/Y2IiB7C1q3A7dtA3bpAz57ytmbN5K9nzqiXFxFZFaMHuDzzzDMAgKFDh+pXbxH/TunQr18//WNJkqDlElRERBVX2AU9blzRiOfmzYHt29mySERmY3SxuHfvXlPkQURE90pIAHbulO+PG1e0vXlz+StbFonITIwuFrt162aKPIiI6F7ffCNPxN21K3Dv9eDshiYiM6vwLNrZ2dmIj49HXl5ese2tWrV66KSIiKyaEMCqVfL9558vvq+wWLx2DUhPB9zczJsbEVkdo4vF69evY/z48fj1119L3c/rFImIHlJ0NHD5sjz6eciQ4vuqVwfq1AFSUoBz54BHH1UnRyKyGkaPhg4PD8ft27dx+PBhODk5Yfv27VizZg0aNWqErVu3miJHIiLrUjiw5bnnABeXkvt53SIRmZHRLYt79uzBjz/+iA4dOkCj0aBevXro2bMn3NzcMHfuXPTt29cUeRIRWYeMDOD77+X793dBF2reHNizhyOiicgsjG5ZzMrKQu3atQEANWrUwPXr1wEALVu2xIkTJ5TNjojI2mzaBGRnA02aAB07ln4MB7kQkRlVaAWX8+fPAwDatGmD5cuXIzExEcuWLYO3t7fiCRIRWZXCLujx44F/57Itgd3QRGRGRndDh4eHIzk5GQAwZ84cPPXUU/juu+9gb2+P1atXK50fEZH1OH8eOHQI0GiA0aMNH1dYLMbGAnfvAk5O5smPiKyS0cXiyJEj9ffbtm2LuLg4nDt3Dv7+/qhZs6aiyRERWZXC6XL69AF8fAwfV6sWUKMGcOuWXGC2aWOW9IjIOhndDX0vIQScnJzwyCOPsFAkInoYBQXAmjXyfUMDWwpJEruiichsKlQsfv3112jRogUcHR3h6OiIFi1a4KuvvlI6NyIi67Fjhzx3Ys2awDPPPPj4wmKRI6KJyMSM7oaePXs2PvvsM0yZMgUd/x2p9/vvv+O1115DXFwc3n//fcWTJCKq8goHtowaBdjbP/h4jogmIjMxulhcunQpvvzySwwfPly/rX///mjVqhWmTJnCYpGIyFjXrwOFixo8qAu6ELuhichMjO6G1mq1aN++fYnt7dq1Q0FBgSJJERFZle++k69ZbNcOaNmyfM8pLBYvXQLy8kyXGxFZPaOLxVGjRmHp0qUltq9YsaLYSGkiIioHIYq6oMvbqggAdesC1arJRealS6bJjYgIFeiGBuQBLjt37sTjjz8OADh8+DASEhIwZswYTJs2TX/cp59+qkyWRERV1YkTwKlTgIMDcM/lPQ8kSfJ1i3/8IXdFF7Y0EhEpzOhi8fTp03jkkUcAAJcvXwYA1KpVC7Vq1cLp06f1x0mGVh4gIqIiha2KgwYB1asb99zmzeVikSOiiciEjC4W9+7da4o8iIisT04OsG6dfN+YLuhCHBFNRGbwUJNyExHRQ9iyBbhzB/D3B554wvjnc0Q0EZmBqsXi/v370a9fP/j4+ECSJGzZsqXYfiEE3n77bfj4+MDJyQkhISH4+++/1UmWiEhphV3QY8cCNjbGP7+wWDx/HtBqlcuLiOgeqhaLWVlZaN26NZYsWVLq/o8++giffvoplixZgqNHj6JOnTro2bMnMjIyzJwpEZHC4uOB336T748bV7Fz1KsHODoCublAbKxiqRER3atCo6GV0qdPH/Tp06fUfUIILFy4EP/3f/+H0NBQAMCaNWvg5eWFdevWYcKECaU+Lzc3F7m5ufrH6enpyidORPSw1qyRp80JCQHq16/YOWxsgKZNgZgYuSu6YUMlMyQiAmDB1yzGxsYiJSUFvXr10m9zcHBAt27dcOjQIYPPmzt3Ltzd3fU3Pz8/c6RLRFR+Oh2wapV8vyIDW+7FNaKJyMQq1LJ44cIFREVFITU1FTqdrti+//73v4oklpKSAgDw8vIqtt3LywtXr141+LxZs2YVm+sxPT2dBSMRWZb9++Vu42rVgMGDH+5cHBFNRCZmdLH45Zdf4tVXX0XNmjVRp06dYvMpSpKkWLF47znvJYQocw5HBwcHODg4KJoDEZGiCge2DBsGODs/3LnYskhEJmZ0sfj+++/jgw8+wJtvvmmKfPTq1KkDQG5h9Pb21m9PTU0t0dpIRFRppKUBmzfL9x+2CxooPn2OEPLKLkRECjL6msXbt2/j2WefNUUuxQQGBqJOnTrYtWuXflteXh727duHTp06mTw+EZFJbNoE3L0rdx8/9tjDn69BA8DWFsjKAhISHv58RET3MbpYfPbZZ7Fz505FgmdmZiImJgYxMTEA5EEtMTExiI+PhyRJCA8Px4cffojIyEicPn0a48aNg7OzM0aMGKFIfCIisyvsgh4/XplWQDs7oHFj+T67oonIBIzuhm7YsCFmz56Nw4cPo2XLlrCzsyu2f+rUqeU+17Fjx9C9e3f948KBKWPHjsXq1avxxhtv4O7du5g4cSJu376Nxx57DDt37kS1atWMTZuISH1nzwKHD8tT3owerdx5mzeXu6HPnAGeekq58xIRoQLF4ooVK+Dq6op9+/Zh3759xfZJkmRUsRgSEgIhhMH9kiTh7bffxttvv21smkRElqdwupy+fYF/r8tWBEdEE5EJGV0sxnKVACIi4+XnA998I98fP17Zc3NENBGZ0ENNyi2EKLNlkIiI/rV9O/DPP0Dt2nLLopLubVnkZzIRKaxCxeI333yDli1bwsnJCU5OTmjVqhW+/fZbpXMjIqo6Cge2jB4tD0pRUuPGgEYD3L4tF6RERAoyuhv6008/xezZszF58mQEBwdDCIGDBw/ilVdewY0bN/Daa6+ZIk8iosorNRX4+Wf5vtJd0ADg5CSvL33pktwVreT1kERk9YwuFhcvXoylS5dizJgx+m0DBgxAUFAQ3n77bRaLRET3W7sWKCgAHn0UCAoyTYxmzeRi8cwZ4J5ZJoiIHpbR3dDJycmlTordqVMnJCcnK5IUEVGVIURRF7QSK7YYcu9KLkRECjK6WGzYsCE2bdpUYvvGjRvRqFEjRZIiIqoyjh0D/v4bcHSU14I2FY6IJiITMbob+p133sFzzz2H/fv3Izg4GJIk4cCBA9i9e3epRSQRkVUrbFUcPBhwdzddHM61SEQmYnTL4uDBg3HkyBHUrFkTW7ZsQUREBGrWrIk//vgDgwYNMkWORESV0927wPr18n1TDGy5V9Om8td//gFu3TJtLCKyKka3LAJAu3btsHbtWqVzISKqWiIjgbQ0oF490w86qVYN8PcH4uPlrujgYNPGIyKrUa5iMT09HW5ubvr7ZSk8jojI6hV2QY8fL8+DaGrNmsnF4pkzLBaJSDHlKharV6+O5ORk1K5dGx4eHpAkqcQxQghIkgStVqt4kkRElU5cHLB7t3x/7FjzxGzeHNixg9ctEpGiylUs7tmzBzVq1AAA7N2716QJERFVCWvWyF+ffBIICDBPTI6IJiITKFex2K1bN/39wMBA+Pn5lWhdFEIgISFB2eyIiCojnQ5YtUq+b8q5Fe/HEdFEZAJGX0QTGBiI69evl9h+69YtBAYGKpIUEVGlFhUFXL0qT5VjzlkiCovFhAQgI8N8cYmoSjO6WCy8NvF+mZmZcHR0VCQpIqJKrXBgy/Dh8rrN5lKjRtG60OfOmS8uEVVp5Z46Z9q0aQAASZIwe/ZsODs76/dptVocOXIEbdq0UTxBIqJK5c4d4Icf5Pvm7IIu1KwZkJIid0V36GD++ERU5ZS7WDx58iQAuWXx1KlTsLe31++zt7dH69atMWPGDOUzJCKqTDZuBHJygKAgoH1788dv3hzYu5fXLRKRYspdLBaOgh4/fjwWLVrE+RSJiEpT2AX9/PNAKZfsmBxHRBORwoxewWVV4Qg/IiIq7u+/gT/+AGxtgVGj1MmBI6KJSGEVWu7v6NGj+P777xEfH4+8vLxi+yIiIhRJjIio0in8Z/qZZ4DatdXJobBl8coVeW1qcw6wIaIqyejR0Bs2bEBwcDDOnDmDyMhI5Ofn48yZM9izZw/c3d1NkSMRkeXLzwe+/Va+r8bAlkK1a8ujooUALlxQLw8iqjKMLhY//PBDfPbZZ/j5559hb2+PRYsW4ezZsxg6dCj8/f1NkSMRkeXbtg1ITQW8vIA+fdTLQ5LYFU1EijK6WLx8+TL69u0LAHBwcEBWVhYkScJrr72GFStWKJ4gEVGlUDiwZcwY+ZpFNRV2RbNYJCIFGF0s1qhRAxn/rgxQt25dnD59GgBw584dZGdnK5sdEVFlkJIC/PKLfH/8eHVzATgimogUZfS/v126dMGuXbvQsmVLDB06FGFhYdizZw927dqFJ5980hQ5EhFZtrVrAa0WePzxoi5gNbEbmogUZHSxuGTJEuTk5AAAZs2aBTs7Oxw4cAChoaGYPXu24gkSEVk0IYrPrWgJClsWL16UB97Y2ambDxFVapIQQqidhCmlp6fD3d0daWlpnEicSAlZWYCrq3w/MxNwcVE3H7UdPgx07ChPUZOSAljC54wQch6ZmXLroiW0dpLRsvKy4DpX/l3LnJUJF3sr/10jxZW3RjL6msVt27Zhx44dJbbv3LkTv/76q7GnIyKq3ArnVhwyxDIKRYAjoolIUUYXizNnzoRWqy2xXafTYebMmYokRURUKWRnA+vXy/ctpQu6EAe5EJFCjC4WL168iOaFH0L3aNq0KS5duqRIUkRElUJEBJCRAdSvD3TtqnY2xbFlkYgUYnSx6O7ujitXrpTYfunSJbhY+7VLRGRdCge2jBsHaIz+ODUtzrVIRAox+tOtf//+CA8Px+XLl/XbLl26hOnTp6N///6KJkdEZLGuXAH27pWvDxw7Vu1sSiosFs+fl6f1ISKqIKOLxQULFsDFxQVNmzZFYGAgAgMD0axZM3h6euLjjz82RY5ERJZnzRr5a8+egCUudRoQADg4ADk5QFyc2tkQUSVm9DyL7u7uOHToEHbt2oU///wTTk5OaNWqFbpa2vU6RESmotUWjYK2hBVbSmNjAzRtCvz5p9wV3aCB2hkRUSVVoQVMJUlCr1690KtXL6XzISKyfHv2AAkJgIcHMHCg2tkY1ry5XCyePQv066d2NkRUSZWrWPzf//6Hl19+GY6Ojvjf//5X5rFTp05VJDEiIouj1QLR0cCcOfLjYcMAR0d1cyoLR0QTkQLKVSx+9tlnGDlyJBwdHfHZZ58ZPE6SJBaLRFQ1RUQAYWHAtWvFt/XsCYSGqpdXWTgimogUUK5iMTY2ttT7RERWISJCXqHl/tVRr1+Xt2/ebJkF470Tcwshj9wmIjKShU0MRkRkYbRauUXx/kIRKNoWHm6Z09M0bAjY2sprRN/bIkr0AFqdFlFxUVh/aj2i4qKg1VngzzeZTblaFqdNm1buE3766acVTuZ+BQUFePvtt/Hdd98hJSUF3t7eGDduHN566y1oLG0CXCKqmqKjyy60hJAHu0RHAyEhZkurXOzsgEaN5JbFM2cAPz+1M6JKIOJsBMK2h+FaetHPva+bLxb1XoTQZhbYgk4mV65i8eTJk+U6maRwF8f8+fOxbNkyrFmzBkFBQTh27BjGjx8Pd3d3hIWFKRqLiKhUycnKHmduzZvLxeLZs8BTT6mdDVm4iLMRGLJpCASKt6QnpidiyKYh2Dx0MwtGK1SuYnHv3r2mzqNUv//+OwYMGIC+ffsCAAICArB+/XocO3ZMlXyIyAp5eyt7nLlxRDSVk1anRdj2MH2haKOrhWrafrhjuxpC0kGChPDt4RjQZABsNDYqZ0vm9FB9uQkJCbhmwutgOnfujN27d+PChQsAgD///BMHDhzA008/bfA5ubm5SE9PL3YjIqqwLl0AX1/D+yVJ7t7t0sV8ORmDI6KpnKLjo4u6noUt6uQugHtBKKppn5E3QSAhPQHR8dEqZklqMLpYLCgowOzZs+Hu7o6AgADUq1cP7u7ueOutt5Cfn69ocm+++SaGDx+Opk2bws7ODm3btkV4eDiGDx9u8Dlz586Fu7u7/ubHa3SI6GHY2ADz55e+r/DSm4UL5eMs0b3FYmmDdIj+lZxxz6UUUgHS7DYAADzyx8BGV7v048gqGF0sTp48GStWrMBHH32EkydP4uTJk/joo4/w9ddfY8qUKYomt3HjRqxduxbr1q3DiRMnsGbNGnz88cdYU7gmaylmzZqFtLQ0/S0hIUHRnIjICt26JX+9vyD09bXcaXMKNW4sF7W3bwOpqWpnQxbMu1rxSykybXYgR3MKGjjCM38SCi9jvP84qvokIYz7V9Pd3R0bNmxAnz59im3/9ddfMWzYMKSlpSmWnJ+fH2bOnIlJkybpt73//vtYu3Ytzp07V65zpKenw93dHWlpaXBzc1MsNyKrlZUFuLrK9zMzARcXdfMxNa1WLriuXAEWLwZatJAHs3h7y13PltqieK+GDYHLl+VlCrt3VzsbKqesvCy4zpV/1zJnZcLF3rS/a1qdFgGLApCYnqi/btFW5wOf3CWQYI8bdp+gRo1LiA2L5TWLVUR5aySjWxYdHR0REBBQYntAQADs7e2NPV2ZsrOzS0yRY2NjA51Op2gcIiKDtmyRC0VPT+D55+XpcYYPl79WhkIRKD45N5EBNhobLOq9CAAgQb7EokCThDu26wEA1fNfwhuPv8tC0QoZXSxOmjQJ7733HnJzc/XbcnNz8cEHH2Dy5MmKJtevXz988MEH+OWXXxAXF4fIyEh8+umnGDRokKJxiIgM+vhj+evEiYCzs7q5VBRHRFM5hTYLxeahm1HXra5+W7ptBPI1sbCBG1ZHZ6mYHamlXFPn3OvkyZPYvXs3fH190bp1awDyKOW8vDw8+eSTCL3n2p2IiIiHSm7x4sWYPXs2Jk6ciNTUVPj4+GDChAn473//+1DnJSIql0OHgMOHAQcH4J7LYSodjogmI4Q2C8WAJgMQHR+N5IxkeFfzRtJNW8z6/hZu3ArA/N2/4M0n+6qdJpmR0cWih4cHBg8eXGybqUYcV6tWDQsXLsTChQtNcn4iojIVtiqOHg14eamby8NgNzQZyUZjg5CAkKINAcCqw0txMcEfX+y+hbEdbqOOW3W10iMzM3qAS2XDAS5ECrOWAS4XLwJNmsjTzZw5U9SVWxllZACFn383bwI1aqibD5WLuQe4PMj1zHR0mBsJaGsiwOcqoqZOVDUfengmG+ACyHMt/vbbb1i+fDkyMjIAAElJScjMzKxYtkREluazz+RC8ZlnKnehCADVqhWtC83WRaqgWq5umPaUPG1ObJIfVv+xX+WMyFyMLhavXr2Kli1bYsCAAZg0aRKuX78OAPjoo48wY8YMxRMkIjK7GzeA1avl+9Onq5qKYtgVTQqY2rUX6tSKgwQN3tl6CRk5d9VOiczA6GIxLCwM7du3x+3bt+Hk5KTfPmjQIOzevVvR5IiIVLF0KXD3LtCuHdCtm9rZKIMjokkh68cPgZDSIAq8MOrbb9VOh8zA6GLxwIEDeOutt0rMqVivXj0kJiYqlhgRkSpycoAlS+T7M2YULelX2XFENCkksIYXRnV2AADEXK6NbWdOqpwRmZrRxaJOp4NWqy2x/dq1a6hWrZoiSRERqWbtWnlZPH9/YMgQtbNRDruhSUHv9RkEN7c4SLBD2MY/kFdQoHZKZEJGF4s9e/YsNpWNJEnIzMzEnDlz8PTTTyuZGxGReel0wCefyPfDwwFbo2cXs1yF3dDx8fLoaKKHoNFosHpMHwhkIz/XFy9v+k7tlMiEjC4WP/30U+zbtw/NmzdHTk4ORowYgYCAACQmJmL+/PmmyJGIyDy2bQPOnQPc3YEXX1Q7G2XVqFE0V+S5c+rmQlXCI7710bttDgBg71+uOHL1osoZkakYXSzWrVsXMTExeP311zFhwgS0bdsW8+bNw8mTJ1G7dm1T5EhEZB6Fk3C//LI83UxVU9i6yK5oUsjnQ0bA3ikBEhzx/Le7oNPp1E6JTMCoYjE/Px/169dHbGwsxo8fjyVLluCLL77Aiy++WGxkNBFRpXPsGLBvn9z1PHWq2tmYBge5kMJsbWzw+bBOEMhHVmY9vPnTZrVTIhMwqli0s7NDbm4upKoyOpCIqFDhtYrDhwO+vurmYiosFskEejZphQ6NbwAANh3W4UJqksoZkdKM7oaeMmUK5s+fjwKOfCKiquLqVeD77+X7VWUS7tKwG5pMZPXIUdDYJUMS1TByTaTa6ZDCjB7qd+TIEezevRs7d+5Ey5Yt4XLfurARERGKJUdEZBaLFgFaLdCjB9C6tdrZmE5hy+KVK/Kk47x8iBTi6uCI9wc2w6zvb+L6zQAs2LMNrz/BGVKqCqNbFj08PDB48GA89dRT8PHxgbu7e7EbEVGlcucO8OWX8v2qvmSplxdQvbo8RdCFC2pnQ1XMiHbBaOQrL86x5LcbSEm/rXJGpBSjWxZXrVplijyIiNSxYgWQmQm0aAH06qV2NqYlSXJX9KFDcld0VW5FJVWsHzcSHeZGQtLWxIg167FnykS1UyIFGN2ySERUZeTlyV3QgHytojUM3uMgFzKhWq5uCOslz+d5OdEPa47uVzkjUgKLRSKyXhs3AklJgLe3PAraGnDZPzKx17r1hlfNOEjQ4O2tF5GZm6N2SvSQWCwSkXUSomgS7qlTAQcHdfMxl8IR0WxZJBNaN24whJQOkV8Ho779Ru106CGxWCQi6/Tbb8BffwEuLsCECWpnYz6FLYsXLgD5+ermQlVWg5p1MCLYDgBw8lJtbD8Xo25C9FBYLBKRdSqchPuFF+QRwtbCzw9wdQUKCoDLl9XOhqqwD54ORTW3OEiww5QNR5DH+ZkrrQoVi/v27UO/fv3QsGFDNGrUCP3790d0dLTSuRERmcZffwE7dgAaDRAernY25iVJQNOm8n12RZMJaTQarBrdGwLZyM/xxSvff6d2SlRBRheLa9euRY8ePeDs7IypU6di8uTJcHJywpNPPol169aZIkciImV9+qn8dcgQIDBQ3VzUwBHRZCbt/RqgV5u7AIDdf7rgaPwllTOiijC6WPzggw/w0UcfYePGjZg6dSrCwsKwceNGzJs3D++9954pciQiUk5SElD4j21VXtqvLBwRTWa09NmRsHdMgAQnjP92J3Q6ndopkZGMLhavXLmCfv36ldjev39/xMbGKpIUEZHJLF4sD+zo0gV49FG1s1EHR0STGdna2GDxsI4QyEdmRj3M+uUHtVMiIxldLPr5+WH37t0ltu/evRt+fn6KJEVEZBIZGcCyZfL9qr60X1kKWxbPnZPXxCYysaeatka7RtcBABsOFeDSjWSVMyJjGL3c3/Tp0zF16lTExMSgU6dOkCQJBw4cwOrVq7GocCUEIiJLtHKlvBZ048bAM8+onY16AgPleSVzcoCrV4H69dXOiKzAN6NGo9X730GXXwcjVkfgjxmT1E6JysnolsVXX30VGzZswKlTpxAeHo6wsDCcPn0aGzduxARrmquMiCqXggJg4UL5/rRp8khoa2VjAzRpIt9nVzSZiauDI94d0AQCOqTeCMAne39VOyUqpwp9Wg4aNAgHDhzAzZs3cfPmTRw4cAADBgxQOjciIuVERABxcUDNmsCYMWpnoz6OiCYVjGrfGQ3qJgAAFu+6jtSMNJUzovIwulisX78+bt68WWL7nTt3UJ9dGURkie5d2m/yZMDJSd18LAFHRJNKNowbCWhuAjpPDFvNuRcrA6OLxbi4OGhLuSA6NzcXiYmJiiRFRKSo6Gjg6FHA0RGYOFHtbCwDR0STSmpXc8fUXrUAAJcT/bD22AGVM6IHKfcAl61bt+rv79ixA+7u7vrHWq0Wu3fvRkBAgKLJEREponBpv7FjgVq11M3FUtzbsiiEvLILkZlMC+mDDcc+R+qNAPz3x/MY2LI9XB0c1U6LDJCEEKI8B2r+vRhckiTc/xQ7OzsEBATgk08+wTMWNsIwPT0d7u7uSEtLg5ubm9rpEFV+WVny2sIAkJkJuLiom8+DnD8vL28nSXJhVDiww9rl5cnvXUEBkJAA+PqqnRHdJysvC65z5d+1zFmZcLG38N81I126kYwnP4mCJNzwSKMkRLzwktopWZ3y1kjl7obW6XTQ6XTw9/dHamqq/rFOp0Nubi7Onz9vcYUiEZF+ab9+/Vgo3sveHmjYUL7PrmhSQcOa3niuo9zBefxiLew496fKGZEhRl+zGBsbi5o1a5oiFyIiZaWmAmvWyPeteRJuQzgimlQ275nBcK12FRLsMGXD7yjgJPEWyYonGiOiKu+LL4DcXHlZv86d1c7G8nBENKlMo9Fg1eheELiLvBw/vPo9R0dbIhaLRFQ1ZWcDn38u358+nQM4SsMR0WQBOvg3xJOtswAAO2OccSzhssoZ0f1YLBJR1fTNN8CNG0BAABAaqnY2lunebujyjXUkMollz46EneM1SHDC+G+3I78gH1FxUVh/aj2i4qKg1bF7Wk1Grw1NRGTxdLqigS2vvQbY8qOuVE2ayC2ut24B168DtWurnRFZKXtbWywe9hgmrI5DRnoAfOcOQKquaDlAXzdfLOq9CKHN+I+fGoxuWTxx4gROnTqlf/zjjz9i4MCB+M9//oO8vDxFkyMiqpCffgIuXgQ8PIDnn1c7G8vl5AQEBsr32RVNKuvdtA18vf8GANhnjYRGVNPvS0xPxJBNQxBxNkKt9Kya0cXihAkTcOHCBQDAlStXMGzYMDg7O+P777/HG2+8oXiCRERGK1za75VXiuaEpNJxRHTZtFogKgpYv17+ytG6JqPVaXG1YAXypKuwgQeq57+o3ycgXyYRvj2cXdIqMLpYvHDhAtq0aQMA+P7779G1a1esW7cOq1evxg8//KB0fkhMTMSoUaPg6ekJZ2dntGnTBsePH1c8DhFVEUeOAAcOAHZ2wJQpamdj+QoHuXBEdEkREfI1r927AyNGyF8DAuTtpLjo+GgkZsbjpt1iCOjgqn0Sjtq2+v0CAgnpCYiOj1YxS+tkdLEohIBOpwMA/Pbbb3j66acBAH5+frhx44aiyd2+fRvBwcGws7PDr7/+ijNnzuCTTz6Bh4eHonGIqAopXNpv5EjAx0fdXCoDtiyWLiICGDIEuHat+PbERHk7C0bFJWckAwDybM4hw+ZnAIBn/iRIwqHU48h8jL7qu3379nj//ffRo0cP7Nu3D0uXLgUgT9bt5eWlaHLz58+Hn58fVq1apd/G9aeJyKArV4DCHo5p09TNpbJgsViSVguEhZU+QrxwHe3wcGDAAMDGxuzpVVXe1bz19+/YfQtn3eOwFXXgkT8at+2/KvU4Mg+jWxY/++wznDhxApMnT8b//d//oeG/y0Vt3rwZnTp1UjS5rVu3on379nj22WdRu3ZttG3bFl9++WWZz8nNzUV6enqxGxFZiYUL5ZHQTz0FtGypdjaVQ9Om8teUFOD2bXVzsRTR0SVbFO8lhLyedjS7Q5XUxb8LfN18IUGCkO7ipp08T2o1bT/Y6xpDggQ/Nz908e+icqbWx+hisXXr1jh16hTS0tIwZ84c/fYFCxbgm2++UTS5K1euYOnSpWjUqBF27NiBV155BVOnTi0zzty5c+Hu7q6/+fn5KZoTEVmoW7eAlSvl+1zar/zc3ABfX/k+r1uUJZezm7O8x1G52GhssKj3IgCABAk5NseRabMXEmzgmTcFELZY2HshbDRszTU3o4vF+vXr4+bNmyW25+TkoHHjxookVUin0+GRRx7Bhx9+iLZt22LChAl46aWX9F3fpZk1axbS0tL0t4SEBEVzIiILtXw5kJUFtG4NPPmk2tlULuyKLs7JqXzHebM7VGmhzUKxeehm1HWrCwC4bfcltEiDvQhEZ885nGdRJUYXi3FxcdCWMnVAbm4urpXVbF8B3t7eaF74IfavZs2aIT4+3uBzHBwc4ObmVuxGRFVcbi7wv//J97m0n/E4IrrIn3/K1yuWRZIAPz+gC7tDTSG0WSjiwuKwd+xerB28DF1a/AMAuJoYhF3n/1I5O+tU7gEuW7du1d/fsWMH3N3d9Y+1Wi12796NwMLJXRUSHByM8+fPF9t24cIF1KtXT9E4RFTJrVsnX3NXty7w3HNqZ1P5sGVR9sMPwJgx8rrideoA/8hFSrGBLoX/iCxcyMEtJmSjsUFIQAgA4LkgHVp+uAxZmfUwacMhnHkrCLb83ptVuYvFgQMHAgAkScLYsWOL7bOzs0NAQAA+KZyyQiGvvfYaOnXqhA8//BBDhw7FH3/8gRUrVmDFihWKxiGiSkyIoulywsIAe3t186mMrL1Y1OmAd98F3nlHftyzJ7BxI7B3r/wzdW+vma+vXChyvXGz0Wg0WDm6J4Yu/Qt5d/0wafM6LH9utNppWRVJCONWjw8MDMTRo0dRs2ZNU+VUzM8//4xZs2bh4sWLCAwMxLRp0/DSSy+V+/np6elwd3dHWloau6SJlJCVVbQqSmYm4OKibj7btwN9+sg5JSTIS/yRcW7eBAo/0zMyrGvVm8xMYOzYonkTX3sN+OijovXEtVp51HNysnyNYpcuZmtRzMrLgutc+b3InJUJF3uVf9dUNm7dGkT9VRMC2Yic3A6P+NZXOyXFaXVaRMdHIzkjGd7VvNHFv4tJB/SUt0YyulisbFgsEinM0orFHj2A3bvlP/KffqpuLpWZlxeQmgocPQq0b692NuYRFyfPlfjXX3KL9LJlwPjxamelx2KxuLyCAgS99zXyc31RzS0Of858FRqN0UMvLFbE2QiEbQ/DtfSilmxfN18s6r3IZAN7ylsjlasb+n//+x9efvllODo64n+FF5EbMHXqVOMyJSKqqJgYuVC0sXnwoAQqW/PmcrF45ox1FIv79skrsdy4IRfKERGAwnMFk7LsbW2x6LlH8eo3V5GRHoDZv0big76D1U5LERFnIzBk0xD9GtiFEtMTMWTTEGweulnVkeDlKhY/++wzjBw5Eo6Ojvjss88MHidJEotFIjKfwmsVn30W4MC3h9OsGRAVZR0jopcvByZPBgoKgEceAbZskUc3k8V7unlbtGlwFH9erou1B3LxYsd/EFhD2dXjzE2r0yJse5i+UJSEEwANhJQFAQEJEsK3h2NAkwGqzTFZrmIxNja21PtERKpJSAA2bJDvT5+ubi5VgTUMcsnPl1ugC+fqHTYM+PprwNlZ3bzIKGtHj0ar99cDBV4YsWozfp8+Se2UHkp0fHRR17OwQa28WbARnki1fxtazXUICCSkJyA6Plo/QtzcHrqzX6vVIiYmBre5TBQRmZpWK7d+rV8vr9JSUACEhFhHt6mpFRaLVbVl8cYNoFcvuVCUJODDD+Upl1goVjrVHJ0wp39DCOiQfD0A/9u/U+2UHkpyxr8rAQmgRv5EOOkega3wgg3cSz9OBUYXi+Hh4fj6668ByIVi165d8cgjj8DPzw9RUVFK50dEJIuIAAICgO7dgREjgE2b5O28zkwZhRNzX74M5OSom4vSTp0COnSQ/9FwdQV+/BGYNYuTt1di4x7tigAfeYW2T3ck42ZWhsoZVZx3NXklILeCoaimfQoCWtyw/wh5mkulHqcGo4vFzZs3o3Xr1gCAn376CXFxcTh37hzCw8Pxf//3f4onSESEiAh5MEJpq0TNnVs07QlVXJ068rRDOh1w4YLa2ShnyxagY0d55HP9+sDhw0C/fmpnRQrYOG44hOYWoK2JYavXqp1OhXXx74LaUl9ULxgDALhltwJ3bf7Q75cgwc/ND1381VsxyOhi8caNG6hTpw4AYNu2bXj22WfRuHFjvPDCCzh16pTiCRKRldNq5evMyprlKzxcPo4qTpKqVle0EMD77wODBsnTPT3xBPDHH0BQkNqZkULquFXHxCdrAAAuJNTFhpO/q5xRxaz4PQqO2S8CANJsI5Bp+4t+nwS59Xth74WqDW4BKlAsenl54cyZM9Bqtdi+fTt69OgBAMjOzoYNl98hIqVFR5feolhICHmwS3S0+XKqqgq7oiv7IJesLHnZx9mz5cdTpsiTt3t6qpsXKe7NJ/uiZo04SLDBfyL+RnZertopGWX7uRjM+/kmJNjB0fUsXGvsKrbf181X9WlzACOW+ys0fvx4DB06FN7e3pAkCT179gQAHDlyBE2bNlU8QSKycsnlvKi7vMeRYVVhRHR8vDzRdkwMYGcHfPEF8OKLamdFJrR23EA89dkB6PK9Me67b7FpfOV4v/9OScAr3/wFSXjC3jEBR6a/CleH18y6gkt5GV0svv3222jRogUSEhLw7LPPwsHBAQBgY2ODmTNnKp4gEVk573Je1F3e48iwyt4NfeCAvGbz9etArVrytaydO6udFZlY09p1MeQx4IfDwJHznthz8TSeaNRC7bTKlJqRhgFf/Aro6gI2qdg2eQDcneQVetSaHqcsXO6PiIxj7uX+tFp5FHRiYunXLUoS4OsLxMaabc3eKuvqVfl7bWcnv892dmpnVH5ffQVMnCjPpdimjTzi2d9f7aweCpf7Kz+dTocWHy5DdmY9ODgl4O+3XoKthX4e5OTnocOCL5GRHgAhpeObF1ujW4PmquTC5f6IqGqwsQEWLZJHQ0tS8YKxcOqThQtZKCrBz08u/rOy5Cl0KsOlRfn58qTsixfLj599Fli1Sv01y8msNBoNvh7VA8OWnULuXT9MiViPpc+OUjutEnQ6HZ764iu5UEQu3g2tq1qhaAwu90dEli80FNi8WR4Vfe9gF19fuVAMVffi7ypDo5EHuRw7JndFW3qxePMmMHQosGeP/Pi994D/+z/On2ilOgY0RrcWh7D/tCO2HXdATKdYtKkbqHZaxYxauxpXk+tBQIdx3SSM7dBV7ZTKhcv9EVHlEBoqD1yIjpYHs3h7A126sEVRaYXF4pkz8rQzlurvv4H+/YErV+RWxLVrgYED1c6KVPbVsFFo/t5KFOTWxZg12xAz81VoNA+9WJ0i3toWgUNn5HWsu7a4gXf6jFc5o/Iz+ju4b98+U+RBRPRgNjby8n7Dh8tfWSgqzxJHRN+7zGNUlDzR9uOPy4ViYCDw++8sFAkAYG9ri8+GtoNAAdLTA/DOjh/VTgkAsPLIPny7X27xDqx7FWtGjFU5I+MYXSz27NkT/v7+mDlzJifhJiKqagrnWrSUEdH3L/PYvbvc4pmZKf/D8McfQMuWamdJFqRfUDu0qp8CAFgdnY2rt66rms+ei6fxzpYUSLCHm1scdrz6ssW0dpaX0dkmJSXhjTfeQHR0NFq3bo1WrVrho48+wrWyJs0lIqLK4d7pc9ReFaesZR4B4NVXgZo1zZsTVQrrxoyBZPsPJJ0Hhq/epFoeF1KT8Pzq45CEK2wdErE3bAzsbcu4AvD+VnS1fwf/ZXSxWLNmTUyePBkHDx7E5cuX8dxzz+Gbb75BQEAAnnjiCVPkSERE5hIYCDg4ADk58lQ6annQMo+SBMyYYTF/TMmyVHN0wlv96gMAklID8PmBXQ94hvJuZ2ei7xc/AdqagM0NbH31aXi6VDP8hNJa0QMC5O0qe6h20MDAQMycORPz5s1Dy5YteT0jEVFlZ2sLNG4s31ezK5rLPNJDeuGxEPh7y//wLPg1ETezMswWO6+gAN3/twb5Ob4QUia+HNMWzev4Gn6CoVb0xER5u8oFY4WLxYMHD2LixInw9vbGiBEjEBQUhJ9//lnJ3IiISA2WMMiFyzySAjaMGwahuQ1oa2HkN9+ZLe7Ty77EnTsBEMjH7P610bNJK8MHl9WKXrgtPFzVVnSji8X//Oc/CAwMxBNPPIGrV69i4cKFSElJwdq1a9GnTx9T5EhEROZkCcVieRcX4zKPVAYf9xqY0N0dAHD2qg++//OIyWOOW7cGl67JqweN6FyAFzt2L/sJlaAV3ehiMSoqCjNmzEBiYiJ++eUXjBgxAs7OzqbIjYiI1KD2iOhLl4A33ij7GEmSV5zp0sU8OVGl9Z+e/eBZPQ4SbDDzh7+QnZdrsljv7vgRe/+qAQB4rGkK5j4z5MFPqgSt6EYXi4cOHcKkSZNQkyPQiIiqpntbFsvbwqeUCxeAbt3ka7Xq1pWLwvtXZOEyj2Skb8cOgJAyoc3zwfPr1pokxrrjB/H1Xi0kaODrFYf1Y8o56XZ5W8dVbEWvXBP9EBGR6TVqJBdhGRly0WYu587JhWJSEhAUBBw/Li/zWLdu8eN8feXtXOaRyql5HV+EPir/4/P7uerYe+lvRc9/IPYcZm2OhwQHuLpexW+TXyrfXIo6HbBzZ9nHWEArOotFIiIqzt4eaNhQvm+urui//5YLxZQUeZLtvXsBLy+5IIyLkx+vWyd/jY1loUhG+2TAUDi5XIUEB7y6bj90Op0i54299Q9Gf/07JOEGG/sk/DZ1OBzt7B/8xIwM+ed47tyibRbais5ikYiISjLnIJe//pJXY0lNBdq0AfbsAWrVKtrPZR5JARqNBl+OegICOcjJ9seUiPUPfc60u1l4anEkREFtCM0tRLzSC3Xcqj/4iVeuAB07Aj/+KM9r+u23wA8/WGwrermLxQsXLpgyDyIisiTmKhZjYoAnngBu3ADatQN27+aqLGQynQOboktQOgDg52N2OJUUX+FzFWi1eOJ/q5F31w8C2fh8VBBa+9R78BP37AE6dJBb0729gf37gVGjLLoVvdzFYtu2bdGsWTO8+eabOHTokClzIiIitZljRPTx43KhePMm8OijwG+/ATVqmC4eEYCvho2ErUMiJLhg9Dc/Vfg8/VZ8iZu3AyBQgBl93fFM80fKfoIQwOefA716AbduyQXjsWPyz34hC21FL3exePPmTXz00Ue4efMmQkND4eXlhRdeeAFbt25FTk6OKXMkIiJzK2xZ/Ptv04yI/uMP4Mkngdu35e64nTsBDw/l4xDdx9HOHh8PaQuBAty5E4C3t28x+hyvbFqLs1f9AAChj93FlC69yn5CXh4wYQIwebI8ufaoUcC+fYCPTwVegfmVu1h0dHREv3798NVXXyE5ORmRkZGoVasWZs6cCU9PTwwYMAArV65EamqqKfMlIiJzaNJEvrj+1i3g+nVlz334MNCzJ5CWBnTuDOzYAbi7KxuDqAwDW3ZAi8AUAMCq/VmIv32j3M/9aM8v+PWEGwCgTcNEfDZoWNlPSE2V/zH68kv5d2rBAuCbbwAnpwrnb24VGuAiSRI6deqEefPm4cyZM4iJiUHXrl2xevVq+Pn54fPPP1c6TyIiMidnZyAgQL6vZFf0wYNyN1x6ujz6+ddfgWrVlDs/UTmtGzMKkm0qJJ0Hhq/eWK7nfP/nEXy+MwcSbFCnVhwinn+x7CfExMjdzQcOAG5uwM8/AzNmlBz1bOEUGQ3dqFEjTJ8+Hfv370dSUhJ69XpAcywREVk+pQe57N8PPPWUPGXIE08Av/wCuLoqc24iI7k7uWBWX3lASuI/AVh6cHeZxx+5ehEzNl6CBEc4uVzFnikvlj2X4ubNQHAwEB8vz1165Ajw9NNKvgSzUXzqHE9PTzRq1Ejp0xIRkbkpWSzu3Qv06QNkZcld0D/9BLi4PPx5iR7Cyx2fgF+dOADA/G0JuJ2dWepx8bdvYNhX+yHpPKCxS8auKc/B2d6h9JPqdMCcOcCzzwLZ2XJL+pEjQNOmJnoVpsd5FomIqHRKjYj+7Tegb1/5D2fv3sDWrXI3N5EFWD9uGITmNqCthRHflFwKMDM3B70Wfw+RXwdCcxubXu4OXw/P0k+WmQkMGQK8+678eNo0uQW9ejnmXrRgtmonQEREFkqJlsXt24GBA4HcXLlg/OEHeRJiIgvh6+GJl0Lc8NUe4EycDzbF/I7aHrlIzkiGl4sX3vzhDHKyAyFwF58Na4z2fg1KP1FcHNC/P3DqlLwK0vLlwLhx5nwpJsNikYiISlfYspicDNy5Y/zUNtu2AYMGydOGDBgAbNzIQpEs0lu9+iPi5Oe4dTsA4ZuOItn+NUDSwT1/BDwKRkBAi6lPuSC01aOln2DfPrlF8cYNeZnKyEh5Sqgqwuhu6ISEBFy7dk3/+I8//kB4eDhWrFihaGJERKQyN7ei5ceM7YreulVuUczLk1eg2LSJhSJZtLFdnKFFBux1DeBWMBAuBT3gUTACAHDL7nME1rlb+hOXLQN69ChahejYsSpVKAIVKBZHjBiBvXv3AgBSUlLQs2dP/PHHH/jPf/6Ddwv76ImIqGqoSFd0ZCQweDCQnw8MHQps2CB3yxFZKK1Oi4+P/Be37b4GALgXjIRn/mQAQJrtRmTZ7kL49nBoddqiJ+XnAxMnAq++ChQUyKuuREfL6zlXMUYXi6dPn8aj/y5Ns2nTJrRo0QKHDh3CunXrsHr1aqXzIyIiNRlbLH7/vTwKtKAAGDEC+O47wM7OdPkRKSA6PhrX0q8hy+Y33NX8CQ0cIMEWWTZRuGP7LQQEEtITEB0fLT/hxg15VP/SpfKciXPnyj/rlWiibWMYXSzm5+fD4d+uhN9++w39+/cHADRt2hTJycnKZkdEROoyZkT0hg1y64pWC4weLa9SYctL48nyJWf8W79IwC27xciXkpCtOYQbdgsB6b7j/vpLnmh73z55QvkffwRmzqx0E20bw+hiMSgoCMuWLUN0dDR27dqF3r17AwCSkpLg6WlgKLlC5s6dC0mSEB4ebtI4RET0r/K2LK5dC4wcKReK48cDq1YBNjamz49IAd7VvPX3CzQpSHJ4GdcdPgSkguLHxVwCOnWSRz43aCAvXdmvn5mzNT+ji8X58+dj+fLlCAkJwfDhw9G6dWsAwNatW/Xd06Zw9OhRrFixAq1atTJZDCIiuk9hsXj1qjyhdmnWrAHGjJEnI37pJeCrr1goUqXSxb8LfN18IRU2I97XSChBgh/c0GXsf+XfgyefBP74o+j3o4ozulgMCQnBjRs3cOPGDaxcuVK//eWXX8ayZcsUTa5QZmYmRo4ciS+//BLVK/nElkRElYqnJ1Crlnz/3LmS+7/+Wm5JFEK+0H/ZMqCsJdCILJCNxgaLei8CgKKC8V8SJEAILNyYDhsBYOpUef7QGjVUyFQdRv9Gr127FjY2NiWKtoCAACxYsECxxO41adIk9O3bFz169Hjgsbm5uUhPTy92IyKih2CoK3r5cuDFF+VCccoU4PPPWShSpRXaLBSb/Wegbmbxn2HfNIHNm4DQS3bAl18CixZZ3bW4Rr/ayZMnw8PDA88880yx7a+99ho2bNigeMG4YcMGnDhxAkePHi3X8XPnzsU777yjaA5ERFatSRP5Yv7NmwE/P6BLF7kFcbI8tQheew345JMqfYE/WYGICIS+8DEGQCC6HpDsCnhnAl2uQm5RfH+O/M+RFTL6X8ANGzZg1KhR2L9/v37blClTsGnTJv38i0pJSEhAWFgY1q5dC0dHx3I9Z9asWUhLS9PfEhISFM2JiMiqRETIE2oD8kTb3bsDNWsWFYqvv85CkSo/rRYICwOEgI0AQuKA4aflrzYC8s/38uXycVbI6JbF3r17Y9myZRg4cCB27tyJlStX4scff8TevXvRuHFjRZM7fvw4UlNT0a5dO/02rVaL/fv3Y8mSJcjNzYXNfRdROzg46Kf2ISKihxARIS9hJkTx7XfuyF9DQ4H581koUuWTlwdcvChfWnHmDBAVBdyzOl0JQgAJCfKk2yEh5srSYlSo033YsGG4ffs2OnfujFq1amHfvn1o2LCh0rnhySefxKlTp4ptGz9+PJo2bYo333yzRKFIREQKuaelxaCjR+UR0PwsJqVptXJhlpwMeHvLlz5U5OcsJwc4f76oKCy8XbxYsVZCK51PulzF4rRp00rdXrt2bbRt2xZffPGFftunn36qTGYAqlWrhhYtWhTb5uLiAk9PzxLbiYhIQdHRZbe0AFbd0kImFBEh/6Ny78+fr688sCQ0tPTnZGXJo/XvLwqvXJH/oSmNm5s8eKt5c3k5yvLM6OLt/eBjqqByFYsnT54sdXuDBg2Qnp6u3y+xK4KIqGoobwuKlba0kIkYuvQhMVHevmYN0KhRyaLw6lXD56xeHQgKKioMC28+PkWXUGi1wM8/y3FKa02XJLlg7dJFuddaiZSrWFR64MrDiIqKUjsFIqKqr7wtKFba0kImUNalD4Xbxowx/PzatUsWhM2by9sf1JhlYyO3XA4ZIh97bw6Fz1240GovuTD6msW0tDRotVrUuG8yylu3bsHW1hZubm6KJUdERCrp0kVuSWFLC5nLnj0PvvQBkCeKb9u2eEHYrJk8Sv9hhIbK00OV1gW+cKHhLnArYHSxOGzYMPTr1w8TJ04stn3Tpk3YunUrtm3bplhyRESkEra0kDnk5gK7dslF2vffl+85ixcDw4ebJp/QUGDAAGUG11QhRs+zeOTIEXTv3r3E9pCQEBw5ckSRpIiIyAIUtrTUrVt8u6+vvN2KW1roIdy9C0RGAqNGyUtJ9usnX4uYnV2+55v60gcbG3nQ1vDh8lcrLxSBCrQs5ubmoqCgoMT2/Px83L17V5GkiIjIQrClhZSQlQVs2yb/k/HLL/LjQnXrAoMHA4MGAaNH89IHC2R0sdihQwesWLECixcvLrZ92bJlxSbPJiKiKqKwpYXIGOnpcmG4eTPw669yi2Ihf3/5EochQ4DHHitaU5yXPlgko4vFDz74AD169MCff/6JJ598EgCwe/duHD16FDt37lQ8QSIiIqok7tyRl4XcvBnYuVO+JrFQ/fpFBWL79qWPUOYgE4tkdLEYHByM33//HQsWLMCmTZvg5OSEVq1a4euvv0ajRo1MkSMRERGZkzErqNy8Cfz4o1zk/fYbkJ9ftK9xY+DZZ+UCsXXr8i0NyUsfLE6Flvtr06YNvvvuO6VzISIiIrWVZwWV1FRgyxa5QNyzp/jSeUFBRS2IQUEVWzuclz5YlHIVi+np6fr5E9PT08s8lvMsEhERVVIPWkHlxReBS5eAffuKL6PXurW8f/Bgec5DqlLKVSxWr14dycnJqF27Njw8PEpd1k8IAUmSoK3IwtxERESkrvKsoPLll0Xb2reXi8PBg+Ul+KjKKlexuGfPHv2KLZa09B8REREpJDq6fCuovPIK8MYbQGCg6XMii1CuYrFbt26l3iciIqIqIjm5fMd17cpC0cqUq1j866+/yn3CVq1aVTgZIiIiUkl5V0Yx9QoqZHHKVSy2adMGkiRBlHYdwz14zSIREVEl1aWLPOqZK6jQfcpVLMbGxpo6DyIiIlKTjQ1XUKFSlatYrFevnqnzICIiIrVxBRUqhdGTcs+dOxdeXl54/vnni21fuXIlrl+/jjfffFOx5IiIiMjMuIIK3Udj7BOWL1+Opk2bltgeFBSEZcuWKZIUERERqahwBZXhw+WvLBStmtHFYkpKCrxLGQlVq1YtJJd32D0RERERVQpGF4t+fn44ePBgie0HDx6Ej4+PIkkRERERkWUw+prFF198EeHh4cjPz8cTTzwBANi9ezfeeOMNTJ8+XfEEiYiIiEg9RheLb7zxBm7duoWJEyciLy8PAODo6Ig333wTs2bNUjxBIiIiIlKP0cWiJEmYP38+Zs+ejbNnz8LJyQmNGjWCg4ODKfIjIiIiIhUZXSwWcnV1RYcOHZTMhYiIiIgsjNEDXIiIiIjIerBYJCIiIiKDWCwSERERkUEsFomIiIjIIBaLRERERGQQi0UiIiIiMojFIhEREREZxGKRiIiIiAxisUhEREREBrFYJCIiIiKDWCwSERERkUEsFomIiIjIIBaLRERERGQQi0UiIiIiMojFIhEREREZxGKRiIiIiAxisUhEREREBll0sTh37lx06NAB1apVQ+3atTFw4ECcP39e7bSIiIiIrIZFF4v79u3DpEmTcPjwYezatQsFBQXo1asXsrKy1E6NiIiIyCrYqp1AWbZv317s8apVq1C7dm0cP34cXbt2LfU5ubm5yM3N1T9OT083aY5EREREVZlFtyzeLy0tDQBQo0YNg8fMnTsX7u7u+pufn5+50iMiIiKqcipNsSiEwLRp09C5c2e0aNHC4HGzZs1CWlqa/paQkGDGLImIiIiqFovuhr7X5MmT8ddff+HAgQNlHufg4AAHBwczZUVERERUtVWKYnHKlCnYunUr9u/fD19fX7XTISIiIrIaFl0sCiEwZcoUREZGIioqCoGBgWqnRERERGRVLLpYnDRpEtatW4cff/wR1apVQ0pKCgDA3d0dTk5OKmdHREREVPVZ9ACXpUuXIi0tDSEhIfD29tbfNm7cqHZqRERERFbBolsWhRBqp0BERERk1Sy6ZZGIiIiI1MVikYiIiIgMYrFIRERERAaxWCQiIiIig1gsEhEREZFBLBaJiIiIyCAWi0RERERkEItFIiIiIjKIxSIRERERGcRikYiIiIgMYrFIRERERAaxWCQiIiIig1gsEhEREZFBLBaJiIiIyCAWi0RERERkEItFIiIiIjKIxSIRERERGcRikYiIiIgMYrFIRERERAaxWCQiIiIig1gsEhEREZFBLBaJiIiIyCAWi0RERERkEItFIiIiIjKIxSIRERERGcRikYiIiIgMslU7AaJKS6sFoqOB5GTA2xvo0gWwsVE7KyIiIkWxWCSqiIgIICwMuHataJuvL7BoERAaql5eRERECmM3NJGxIiKAIUOKF4oAkJgob4+IUCcvMjmtTououCisP7UeUXFR0Oq0aqdERGRybFkkMoZWK7coClFynxCAJAHh4cCAAeySrmIizkYgbHsYrqUX/ZPg6+aLRb0XIbQZW5OJqOpiyyKRMaKjS7Yo3ksIICFBPo6qjIizERiyaUixQhEAEtMTMWTTEEScZWsyEVVdLBarAq0WiIoC1q+Xv2rZNaY4IYCDB4G33irf8VevmjYfMhutTouw7WEQKNmaXLgtfHs4u6SJqMpisVjZRUQAAQFA9+7AiBHy14AAXjenlIwMYOlSoHVroHNnuWAsj6lTgTfeAC5fNm1+ZHLR8dG4ln4NknCCc0E3eOaFwT1/GOx0fgDkgjEhPQHR8WxNJqKqidcsVmaFAy3uv36ucKDF5s0cmVtRf/0lF4lr1wKZmfI2R0fgueeAX38Frl8v/bpFQL5WMT0dWLBAvj31FPDqq0DfvoAtf+Uqk6u3ruPTPQdQK3c2nHSPQIKdfp9HwSjkSfHItjmIbJuDSExPUjFTIiLTkYQw9BevakhPT4e7uzvS0tLg5uamdjrK0WrlFkRD189JkjyVS2wsB1qUV04O8P33cpH4++9F25s0AV55BRg7FqhevahIB4oXjJIkf924EbC3B5YtA3bsKDrG1xd4+WXghRcAHx/zvCZTyMoCXF3l+5mZgIuLuvko7PKNFCzctwd7z95BRqZPsQIxX0pAts0fsNP5wUnXttg+YfMPgvzyML5jWwxu2QEaDTtu6OFk5WXBda78u5Y5KxMu9lXrd43UV94aicViZRURAQwe/ODj9u4FQkJMnk6ldukSsHw5sGoVcPOmvM3WFhg0SG4RDAkpKgQLlTbPop8fsHBh8dbcK1fkc69cCdy4UXTuAQPkcz/xRMlzW7oqWCyeS03Eon17se9cOrKyfCGh6B8sjV0ysjQHcQd7kSddBf59uyThDGfto3DWBsNJ1w4S7ItOaHMDjetmY8yjLTHikU4sHKlCWCySqbFY/FeVKRYTE4F9+4pu58+X73kNG8pFZbduQHAwUJm/B0oqKAB+/lluRdy5s2i7n19R65+3d9nnMGYFl5wc4Icf5Hj3XvfYuLHcajlunNxqWRlUkWLxVFI8Fu6LwsGLWbibXbdYgWhjn4S2ARImBD+Knk1a6UdDAyg20EX6t3L86plvcSnZEb+eTsGtO96Q4KA/RmhuoaFPJkZ2aI4x7TvDli39VE4sFsnUWCz+q9IWi1evFi8OlRgoodEAjzwiF45du8rFTWUpUJSSlAR89RXw5ZdFrYKSVHRd4dNPm/66wr/+kruov/22+PWQw4bJOXToYNmtjZW4WDyWcBmL90fj90s5yL1bF9I9Y/xsHRLRPtAGr3bpiG4Nmpd4bmnzLPq5+WFh74XF5lm8npmOJdG78fOpJNy45QUJTvp9QnMHgXXSMLx9U4x/tCvseQ0rlYHFIpkai8V/VYpiUQi5GLy3OIyPL37M/YVep05A27Zyi2Npb6EkAXXqAB98ABw4UHrBKUlAq1byOQvPW7Om6V6nWnQ6YM8euVXvxx+LphaqWRN4/nlgwgSgfn3z55WRAXz3nZzXX38VbX/kEbloHD7cMguxSlYs/h53AUv2H8QfV/KQn+NbbJ+d4zU81sAek7oEo2NA4weeS6vTIjo+GskZyfCu5o0u/l1gozHcUng7OxOfH9iDrX8l4J8btSHBWb9PSGnw97qNoe0a4aXHQ+BoZ2/wPGSdWCySqbFY/Je5ikVtfh6if/kCyf9chrdXA3TpOxE2hj78hZC7ke8tDpPuG0lpawu0b19UyJXWhfzvQAutJBDtDyS7At6ZQJd4wEZIJUdDX7sG7N9fdld2UFDx4rFOHeVet8IeGPvWLWD1arkV7+LFou2dO8vF2ODBgINDifOWO76RhYNBQgCHD8tF46ZNQG6uvN3NDRgzRs61efGWLjW/72oWi+X9nu+7fAZfRP+O47FaFOTW1W8X0MHRKREdGzlhatcueMTXfP8kZOTcxdJDexB5Mg5JN2pCEq5FeUkZqFvrJga3rY8JnbrD1cGxxPMV+3mrADVjqx1fzdgsFsnUqlSx+MUXX2DBggVITk5GUFAQFi5ciC5dupTrueYoFiNWvYGwM5/immvRpLy+mTZY1HwaQsd/JLdsnTlTVKTt3w/880/xk9jbA48+WlSodexY9Af5YWKXJSWlePH4998lj2ncuCinbt3kEb1KxH5IBmM3ew2hQUPkwmvjRvlaQQCoVg0YPVq+PrBly4ePb6ql327cKCpw720J7tpVLhpDQxHx3Vuqfd8BqFYsPuh7vuv8X1h28AhOxkrQ5RddbyqghZNzIro0dsHUriFo6eNvlnzLkp2Xi+W/78XmE5dxLbUGJFH02SSkLNTxvI6BbephcucnUc3RSdWlBtVe5tCaXzuLRTK1KlMsbty4EaNHj8YXX3yB4OBgLF++HF999RXOnDkDf/8Hf+ibuliMWPUGhlxdIF/yfs9lZtK/39XNJxoi9NDtolG2hRwdgccfLyrCHn8ccHKCMQovur9/ZYnCi+43D91s3Afa9evygI3C4vGvv0p2cdevD3TrhojqKRhS7VfDr7ve6yYrXB74Pd8EhJ79d2Pr1nKRNWKEXDAqEV/p73tpdDrgt9/konfrVvkxgIhHnDCk311Vvu96KhSLpX7PBWAv6sFJGwx3EQJJ63PPLi1cXK8hpIk7pnYLQdPadUs5q2XIyc/D10f2YePxi7ia4gFJuOv3CWTDpVos4nIjkaM5ASHl6fcp+vNmgFl+1i00vtqvHWCxSKZXZYrFxx57DI888giWLl2q39asWTMMHDgQc+fOfeDzTVksavPzEPAfZ1xz0cJeNICDtmSLlVMB0OMyINnYADVqADU9AU9PwKO6fB1iBQkI/HZ5F+4W5Bg8xsnWCT0a9NB/uBktPw+4eUsudG/eANLSAAEIAL81AO6WcW2+UwHQw6EZJEnZKUOE0OG33LMPjp1bF1JgfaC6B1DR119afHN83+939y5w9SrE1Tj85ptX/LVLWmTY/iTfFYBvlg1iP8w2bZe0mYtFrU6LgEUB+tYdO10gXLTBcNYGw0746Y8TyEe1akl4oml1hHXrjgY1y76EwhLlFRTgm6PRWHfsLC4nu0HSFQ1A0+Eu7tocRa50EbingFH85+1fqvysW0j80mLrpDRk2e4FIBeMvm6+iA2LNWmXNItFMrUqUSzm5eXB2dkZ33//PQYNGqTfHhYWhpiYGOzbt6/Ec3Jzc5FbeM0XgLS0NPj7+yMhIUHxYjH65y/wzKlZAABXbR9Uzx+v6PmJHkSHXCQ6ji627eeWc9HlmYmmC5qVVTSpeFKSyYvF6KvReGbdM/rHnnnT4ax7DAAgkIe7mj9x1+YwPh80GaFBvU2aizkVaLX4YM9arDx8FE7ax2EraqmdklXLk2Lxj8Obxbb9POJndKlXvkuiKiIrLws+n8i/a0nTk1gskuLS09Ph5+eHO3fuwN3d3fCBwoIlJiYKAOLgwYPFtn/wwQeicePGpT5nzpw5AvK/3bzxxhtvvPHGG2+8PeCWkJBQZj1WKSb5ku6bc04IUWJboVmzZmHatGn6xzqdDrdu3YKnp6fB5yilsEI3RSsmY1tebLXjMzbfc2uJrXZ8xuZ7XlVjCyGQkZEBnwcsQWvRxWLNmjVhY2ODlJSUYttTU1Ph5eVV6nMcHBzgcN90KB4eHqZKsVRubm6qzenI2Oqw1tdurbHVjm+tsdWOz9jqsNbXbq7YZXY//8uiFyy1t7dHu3btsGvXrmLbd+3ahU6dOqmUFREREZH1sOiWRQCYNm0aRo8ejfbt26Njx45YsWIF4uPj8corr6idGhEREVGVZ/HF4nPPPYebN2/i3XffRXJyMlq0aIFt27ahXr16aqdWgoODA+bMmVOiG5yxq2ZsteMzNt9za4mtdnzG5ntuLbENseipc4iIiIhIXRZ9zSIRERERqYvFIhEREREZxGKRiIiIiAxisUhEREREBrFYJCIiIiKDWCySIqKionD37l210zC73NxcXL58Gbm5uWqnYnb//PNPidWVTEmr1eKff/7BjRs3zBaztPipqanQarWq5EBkbvxst77P9tKwWHwIf/75J95//3188cUXJf6Apaen4/nnnzdZ7K+++gpjx47FqlWrAAAbN25Es2bNUL9+fcyZM8dkcQ3p1asX4uLiTBrjwoULuHempwMHDmDgwIEICgpCjx498OOPP5o0/urVq3H48GEAQE5ODl588UW4uLigcePGcHV1xSuvvGKyD5aWLVvivffeQ0JCgknOX5Zbt25h8ODBqFevHiZNmgStVosXX3wR3t7eqFu3Ljp16oTk5GSTxf/ll1/QtWtXuLi4wMfHB15eXvDw8MDo0aMRHx9vsriFIiMjERwcDGdnZ/j4+MDb2xvOzs4IDg7Gli1bTB7fkLNnz6J+/fomO7+an28PUpVfuzV+tgPqfr5b62e7UQRVyI4dO4S9vb0ICgoS/v7+ombNmmLPnj36/SkpKUKj0Zgk9meffSZcXFxEaGio8Pb2Fu+//77w9PQU77//vnj33XeFu7u7WL58uUlit23bttSbJEmiWbNm+semoNFoxD///COEEGLv3r1Co9GIfv36iQ8++EAMHjxYaDQasX37dpPEFkKIhg0biqNHjwohhJgxY4YICAgQERER4uzZs2LLli2icePG4vXXXzdJbEmShKenp7CxsRFPPfWU2Lx5s8jPzzdJrPuNHz9etGjRQixevFh069ZNDBw4ULRq1UocOHBAHDp0SHTo0EGMGTPGJLG/+eYbUa1aNREeHi5mzpwpvLy8xMyZM8XSpUtFt27dRM2aNcWFCxdMElsIIZYtWybs7e3FK6+8IiIjI8WhQ4fEwYMHRWRkpHjllVeEg4ODWLFihcnilyUmJsZknzFqfr6VR1V97db62S6Eup/v1vrZbgwWixXUsWNH8Z///EcIIYROpxMfffSRcHV1Fb/++qsQwrQfKE2bNhXfffedEEKIEydOCFtbW/HVV1/p969cuVK0a9fOJLFtbW1F7969xdtvv62/zZkzR2g0GjFx4kT9NlOQJEn/YfLkk0+KiRMnFts/c+ZM0bVrV5PEFkIIBwcHcfXqVSGEEI0bN9a/14X27dsn/P39TRJbkiSRmJgoIiMjRb9+/YStra2oVauWmD59ujhz5oxJYhby9vYWBw8eFELIP9eSJImdO3fq9x84cEDUrVvXJLGbNm0qNmzYoH989OhR4evrK3Q6nRBCiOeee04MGjTIJLGFEKJBgwbFfrfu9/XXX4v69eubJPZrr71W5m3UqFEm+4xR8/NNCOt97db62S6Eup/v1vrZbgwWixXk5uYmLl26VGzbunXrhIuLi9i6datJP1CcnJz0P9hCyD/op0+f1j++ePGi8PDwMEnsAwcOiAYNGoj//ve/QqvV6rfb2tqKv//+2yQxC937YeLt7S0OHz5cbP/ff/8tPD09TRa/Xr16+haGunXr6v8TLXTmzBnh4uJiktj3vnYhhEhOThYffvihaNSokdBoNKJjx47i66+/NklsZ2dnERcXp39sZ2cnTp06pX985coVk71uJycnERsbW2ybra2tSExMFEIIceTIEZP9rAshhKOjozh37pzB/WfPnhWOjo4mia3RaMQjjzwiQkJCSr21b9/eZJ8xan6+CWG9r91aP9uFUPfz3Vo/243BYrGCatWqJY4dO1Zi+4YNG4Szs7NYunSpyT5QPD09i/3H4evrW+yP+cWLF4Wrq6tJYgshRFpamhg2bJh49NFH9R+q5ioWL126JNLS0kT9+vXFyZMni+2/ePGicHZ2Nln8//znP6Jjx47i9u3bYubMmaJfv34iIyNDCCFEVlaWGDp0qOjVq5dJYt/bRXO/vXv3ilGjRpnsw6x169ZiyZIlQgghtm3bJqpVqyY++eQT/f6lS5eKFi1amCR2s2bNxPfff69/fPz4cWFvby8KCgqEEPJ7bqrXLYQQ7dq1E9OmTTO4f9q0aSZr6WnSpIn49ttvDe4/efKkyT5j1Px8E8J6X7u1frYLoe7nu7V+thuDxWIF9ezZUyxYsKDUfevWrRN2dnYm+0AJDg4u1jV3v59++slkf7zvtXLlSlGnTh2xfPlyYWdnZ5ZiUaPRCI1GIyRJKtE9uGXLFtGoUSOTxc/NzRX9+/cX1atXFz179hSOjo7C2dlZNGrUSLi4uAh/f39x/vx5k8S+/7/P0qSlpZkk9tq1a4WNjY1o2LChcHR0FJs3bxY+Pj5i6NChYtiwYcLe3l5fTCptyZIlwt3dXbzxxhviv//9r/Dx8REvvPBCsdxMeR1VVFSUcHFxEc2bNxfh4eFi7ty5Yt68eSI8PFwEBQUJV1dXsX//fpPEHjFihAgPDze4PyYmRkiSZJLYan6+CWG9r91aP9uFUPfz3Vo/241hq/YAm8rq1Vdfxf79+0vdN3z4cADAihUrTBJ7/vz5cHFxMbg/Pj4eEyZMMEnse40fPx6dO3fGyJEjUVBQYPJ4e/fuLfbY29u72OO4uDi89NJLJotvb2+PH3/8Edu3b8dPP/0EGxsb6HQ6eHt7Izg4GCNGjCjzfXkYY8eOhZOTU5nHuLm5mST2yJEjUa9ePRw5cgSdOnVCx44d0axZM8ybNw/Z2dlYsWIFxo4da5LYkyZNgkajwdq1a5Gbm4tx48Zh9uzZ+v2PPvoo1q1bZ5LYANCtWzecPn0aS5cuxeHDh/VTBdWpUwfPPPMMXnnlFQQEBJgk9ieffFLmCMzWrVtDp9OZJLaan2+A9b52a/1sB9T9fLfWz3ZjSELcM1adqAJ0Oh0yMjLg5uYGSZLUToeIiBTAz3YqxJZFBVy9ehUpKSmQJAleXl6oV6+eVcZ2d3dXLbY5X7fa8Rlbnfec1GGtP3OWFNucn+2lxbfW77tFfcap3Q9emX366afC19dXf41F4TUXvr6+4rPPPmPsKhZb7fiMrc57XhZTzvdn7bGt9WfOWmOrHd9aY5cXi8UKevfdd4Wbm5uYN2+eOHnypEhKShKJiYni5MmTYt68ecLd3V289957jF1FYqsdn7HVec8fxJQDLaw5trX+zFlrbLXjW2tsY7BYrCBfX18RGRlpcH9ERITw8fFh7CoSW+34jG3+2EIIMWjQoDJvTzzxhMla2Kw1thDW+zNnrbHVjm+tsY3BaxYr6ObNm2jSpInB/Y0bN8bt27cZu4rEVjs+Y5s/NgD89NNP6NmzJ7y8vErdr9VqGdsErPVnzlpjqx3fWmMbRe1qtbLq1q2bGDlyZKlrOObn54sRI0aIbt26MXYVia12fMZW5z1v2bJlmcv9mXJyaGuNLYT1/sxZa2y141trbGNw6pwKOnXqFHr16oXc3Fx069YNXl5ekCQJKSkp2L9/PxwcHLBr1y4EBQUxdhWIrXZ8xlbnPR8/fjycnZ3x+eefl7r/7NmzePrppxEbG8vYCrLWnzlrja12fGuNbQwWiw8hIyMDa9euLTFZb8eOHTFixAiTTqTJ2OaPrXZ8xjZ/7NzcXGi1Wjg7O5ssBmOXzlp/5qw1ttrxrTV2ebFYJCIiIiKDNGonUJX07dsXycnJjG0lsdWOz9jqsNbXzu87Y1tTfGuNbQiLRQXt378fd+/eZWwria12fMZWh7W+dn7fGdua4ltrbENYLBIRERGRQSwWFVSvXj3Y2dkxtpXEVjs+Y6vDWl87v++MbU3xrTW2IRzgQkREREQGcQWXh3Tx4kUcOnQIKSkpkCQJXl5e6NSpExo1asTYVTC22vEZm++5tcQuS1ZWFo4fP46uXbsythXEVju+tcYuRr35wCu3O3fuiP79+wtJkoSHh4do3LixaNSokfDw8BAajUYMGDBApKWlMXYVia12fMbme24tscsjJibGpCvIMLZlxVY7vrXGvheLxQoaPXq0aNmypTh8+HCJfYcPHxatWrUSY8aMYewqElvt+IzN99xaYpeHtf7xttbYase31tj3YrFYQe7u7qV+kBb6/fffhbu7O2NXkdhqx2ds88dWO761xhZCiOrVq5d5c3NzM9kfUMY2f2y141trbGPwmsWHIElShfYxduWMrXZ8xjZ/bLXjW2vs3NxcvPrqq2jZsmWp+69evYp33nmHsatIbLXjW2tso6hdrVZWo0aNEq1atRJHjx4tse/o0aOiTZs2YvTo0YxdRWKrHZ+x+Z5bS2whhOjUqZNYuHChwf2m7JpjbPPHVju+tcY2BovFCrp9+7bo3bu3kCRJVK9eXTRp0kQ0bdpUVK9eXWg0GtGnTx9x+/Ztxq4isdWOz9h8z60lthBCfPDBB+Ltt982uD8+Pl6MGzeOsatIbLXjW2tsY3CexYd07tw5/P7770hJSQEA1KlTBx07dkTTpk0ZuwrGVjs+Y/M9t5bYRGQ5WCwSERERkUFc7u8h6XQ6g9vj4+MZu4rFVjs+Y5s/ttrxrTW22vEZ2/yx1Y5vrbHLg8ViBaWnp2Po0KFwcXGBl5cX5syZA61Wq99//fp1BAYGMnYVia12fMbme24tsdWOz9h8z60ltlHUvWSy8po6dapo3Lix+P7778WXX34p6tWrJ/r27Styc3OFEEKkpKQISZIYu4rEVjs+Y/M9t5bYasdnbL7n1hLbGCwWK8jf31/s3btX//jGjRviscceE7169RI5OTkiJSXFZMPdGdv8sdWOz9jmj612fGuNrXZ8xjZ/bLXjW2tsY7AbuoJu3LiBevXq6R97enpi165dyMjIwNNPP43s7GzGrkKx1Y7P2OaPrXZ8a42tdnzGNn9steNba2xjsFisID8/P5w9e7bYtmrVqmHnzp24e/cuBg0axNhVKLba8Rnb/LHVjm+tsdWOz9jmj612fGuNbQwWixXUq1cvrFq1qsR2V1dX7NixA46OjoxdhWKrHZ+xzR9b7fjWGlvt+Ixt/thqx7fW2EZRux+8srp165Y4ffq0wf0ZGRkiKiqKsatIbLXjM7b5Y6sd31pjqx2fsc0fW+341hrbGJyUm4iIiIgMslU7gcosKysL69atw6FDh5CSkgJJkuDl5YXg4GAMHz4cLi4ujF2FYqsdn7H5nltLbLXjMzbfc2uJXV5sWaygM2fOoGfPnsjOzka3bt3g5eUFIQRSU1Oxb98+uLi4YOfOnWjevDljV4HYasdnbL7n1hJb7fiMzffcWmIbxZx93lVJSEiIGDZsmH7izHvl5uaK4cOHi5CQEMauIrHVjs/YfM+tJbba8Rmb77m1xDYGi8UKcnJyEn///bfB/adOnRJOTk6MXUViqx2fsc0fW+341hpb7fiMbf7Yase31tjG4NQ5FVS9enVcvHjR4P5Lly6hevXqjF1FYqsdn7HNH1vt+NYaW+34jG3+2GrHt9bYRlG7Wq2s5syZI9zd3cWCBQtETEyMSE5OFikpKSImJkYsWLBAVK9eXbzzzjuMXUViqx2fsfmeW0tsteMzNt9za4ltDBaLD2HevHnC29tbSJIkNBqN0Gg0QpIk4e3tLebPn8/YVSy22vEZm++5tcRWOz5j8z23ltjlxdHQCoiNjUVKSgoAoE6dOggMDGTsKhxb7fiMzffcWmKrHZ+x+Z5bS+wHYbFIRERERAZxgMtDuHv3Lg4cOIAzZ86U2JeTk4NvvvmGsatQbLXjMzbfc2uJrXZ8xuZ7bi2xy03dXvDK6/z586JevXr6awy6desmkpKS9PtTUlKERqNh7CoSW+34jM333Fpiqx2fsfmeW0tsY7BlsYLefPNNtGzZEqmpqTh//jzc3NwQHByM+Ph4xq6CsdWOz9h8z60lttrxGZvvubXENora1WplVbt2bfHXX38V2zZx4kTh7+8vLl++bNL/Bhjb/LHVjs/Y5o+tdnxrja12fMY2f2y141trbGPYql2sVlZ3796FrW3xb9/nn38OjUaDbt26Yd26dYxdhWKrHZ+xzR9b7fjWGlvt+Ixt/thqx7fW2EZRu1qtrDp06CC++eabUvdNmjRJeHh4mOy/AcY2f2y14zO2+WOrHd9aY6sdn7HNH1vt+NYa2xgsFivoww8/FH369DG4/9VXXxWSJDF2FYmtdnzGNn9steNba2y14zO2+WOrHd9aYxuD8ywSERERkUEcDU1EREREBrFYJCIiIiKDWCwSERERkUEsFomIiIjIIBaLREQKiIqKgiRJuHPnjtqpEBEpiqOhiYgqICQkBG3atMHChQsBAHl5ebh16xa8vLwgSZK6yRERKYgruBARKcDe3h516tRROw0iIsWxG5qIyEjjxo3Dvn37sGjRIkiSBEmSsHr16mLd0KtXr4aHhwd+/vlnNGnSBM7OzhgyZAiysrKwZs0aBAQEoHr16pgyZQq0Wq3+3Hl5eXjjjTdQt25duLi44LHHHkNUVJQ6L5SICGxZJCIy2qJFi3DhwgW0aNEC7777LgDg77//LnFcdnY2/ve//2HDhg3IyMhAaGgoQkND4eHhgW3btuHKlSsYPHgwOnfujOeeew4AMH78eMTFxWHDhg3w8fFBZGQkevfujVOnTqFRo0ZmfZ1ERACLRSIio7m7u8Pe3h7Ozs76rudz586VOC4/Px9Lly5FgwYNAABDhgzBt99+i3/++Qeurq5o3rw5unfvjr179+K5557D5cuXsX79ely7dg0+Pj4AgBkzZmD79u1YtWoVPvzwQ/O9SCKif7FYJCIyEWdnZ32hCABeXl4ICAiAq6trsW2pqakAgBMnTkAIgcaNGxc7T25uLjw9Pc2TNBHRfVgsEhGZiJ2dXbHHkiSVuk2n0wEAdDodbGxscPz4cdjY2BQ77t4Ck4jInFgsEhFVgL29fbGBKUpo27YttFotUlNT0aVLF0XPTURUURwNTURUAQEBAThy5Aji4uJw48YNfevgw2jcuDFGjhyJMWPGICIiArGxsTh69Cjmz5+Pbdu2KZA1EZHxWCwSEVXAjBkzYGNjg+bNm6NWrVqIj49X5LyrVq3CmDFjMH36dDRp0gT9+/fHkSNH4Ofnp8j5iYiMxRVciIiIiMggtiwSERERkUEsFomIiIjIIBaLRERERGQQi0UiIiIiMojFIhEREREZxGKRiIiIiAxisUhEREREBrFYJCIiIiKDWCwSERERkUEsFomIiIjIIBaLRERERGTQ/wN/qzP6Esx8kAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evolution of clicks relative to replication visits, Figure 5 used in the original working paper version but not latest version\n",
    "fig, ax = plt.subplots(constrained_layout=True)\n",
    "\n",
    "labels=[ '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07' ]\n",
    "time=list(range(1,19))\n",
    "import matplotlib.pyplot as plt\n",
    "x = time\n",
    "y = sharerepl.copy()\n",
    "y[6]=None\n",
    "y[13]=None\n",
    "plt.plot(x, y, marker='o', color='red');\n",
    "\n",
    "\n",
    "x = time\n",
    "y = share2repl.copy()\n",
    "y[6]=None\n",
    "y[13]=None\n",
    "plt.plot(x, y, marker='o', color='green');\n",
    "\n",
    "\n",
    "plt.ylim(0, 15);\n",
    "plt.xticks(x, labels, rotation='vertical')\n",
    "plt.axvline(x=7, color='red')\n",
    "plt.axvline(x=14, color='green')\n",
    "\n",
    "# Pad margins so that markers don't get clip\n",
    "ax.plot(x, y)\n",
    "ax.set_xlabel('time')\n",
    "ax.set_ylabel('clicks / visits to replication papers')\n",
    "#ax.set_title('                                                  Figure 5: the percentage increase in visits to replications caused by linking originals to replications')\n",
    "ax.legend(['Group 1', 'Group 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# evolution of clicks relative to replication visits, FIGURE 5 in the latest version of the paper\n",
    "import numpy as np\n",
    "from matplotlib import pylab as pl\n",
    "import random\n",
    "import numpy\n",
    "\n",
    "labels=[ '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07' ]\n",
    "X=range(1,19)\n",
    "\n",
    "y1 = sharerepl.copy()\n",
    "y1[6]=0\n",
    "y1[13]=0\n",
    "\n",
    "y2 = share2repl.copy()\n",
    "y2[6]=0\n",
    "y2[13]=0\n",
    "\n",
    "# plot the bars\n",
    "pl.bar(X, y1, color = 'black')\n",
    "pl.bar(X, [ -x for x in y2], color = 'grey')\n",
    "#plt.title(\"Back-to-Back Bar Chart\")\n",
    "plt.axvline(x=7, color='red', linestyle='--')\n",
    "plt.axvline(x=14, color='red', linestyle='--')\n",
    "\n",
    "pl.xticks(X, labels, rotation='vertical')\n",
    "\n",
    "pl.yticks([10,7.5,5,2.5,0,-2.5,-5,-7.5],[10,7.5,5,2.5,0,2.5,5,7.5])\n",
    "pl.grid(which='major',  linestyle='--')\n",
    "pl.xlabel('Time')\n",
    "pl.ylabel('Clicks / Visits to Replication Papers (in Percent)')\n",
    "\n",
    "pl.show()\n",
    "\n",
    "#pl.text(-20,175, 'Group 1', fontsize=12, color='black')\n",
    "#pl.text(10,175, 'Group 2', fontsize=12, color='blue')\n",
    "\n",
    "#pl.axvline(0.0)\n",
    "#\n",
    "#pl.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "orig2020-02    229\n",
       "orig2020-03    262\n",
       "orig2020-04    279\n",
       "orig2020-05    252\n",
       "orig2020-06    351\n",
       "orig2020-07    253\n",
       "orig2020-08    242\n",
       "orig2020-09    206\n",
       "orig2020-10    274\n",
       "orig2020-11    287\n",
       "orig2020-12    234\n",
       "orig2021-01    242\n",
       "orig2021-02    245\n",
       "orig2021-03    306\n",
       "orig2021-04    336\n",
       "orig2021-05    328\n",
       "orig2021-06    247\n",
       "orig2021-07    171\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now focus on total visits to replication pages of treated and not treated\n",
    "# so here we do not subtract visits due to clicks\n",
    "# this is the kind of analysis we could have done if we didnt have click data\n",
    "replsumtreated=replvisits[data['randomized']==1].sum(axis=0)\n",
    "replsumnottreated=replvisits[data['randomized']==0].sum(axis=0)\n",
    "replsumnottreated\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[283, 319, 307, 283, 205, 245, None, 235, 308, 336, 276, 272, 267, None, 377, 336, 239, 192]\n",
      "[229, 262, 279, 252, 351, 253, None, 206, 274, 287, 234, 242, 245, None, 336, 328, 247, 171]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this is to create the graphs for the total visits to replications - FIGURE 6 in the paper\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#fig, ax = plt.subplots(constrained_layout=True)\n",
    "labels=[ '2020-02','2020-03','2020-04','2020-05','2020-06','2020-07','2020-08','2020-09','2020-10','2020-11','2020-12','2021-01','2021-02','2021-03','2021-04','2021-05','2021-06','2021-07' ]\n",
    "time=list(range(1,19))\n",
    "x = time\n",
    "#y = origsumtreated.to_list()\n",
    "y = replsumtreated.to_list()\n",
    "y[6]=None\n",
    "y[13]=None\n",
    "y1=y.copy()\n",
    "print(y)\n",
    "plt.plot(x, y, color='black', linewidth=5);\n",
    "\n",
    "\n",
    "x = time\n",
    "#y = origsumnottreated.to_list()\n",
    "y = replsumnottreated.to_list()\n",
    "y[6]=None # we put the months during which we made changes to missing\n",
    "y[13]=None  # we put the months during which we made changes to missing\n",
    "y2=y.copy()\n",
    "print(y)\n",
    "plt.plot(x, y, color='grey', linewidth=5);\n",
    "\n",
    "\n",
    "plt.ylim(0, 500);\n",
    "plt.xticks(x, labels, rotation='vertical')\n",
    "plt.axvline(x=7, color='red')\n",
    "plt.axvline(x=14, color='red')\n",
    "\n",
    "# Pad margins so that markers don't get clip\n",
    "\n",
    "#ax.legend()\n",
    "\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Visits to replications')\n",
    "#ax.set_title('Figure 6: visits to replication pages, treated versus controls')\n",
    "plt.legend(['Group 1', 'Group 2'])\n",
    "plt.grid(which='major',  linestyle='--')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1642\n",
      "1626\n",
      "1.009840098400984\n",
      "1694\n",
      "1488\n",
      "1.1384408602150538\n",
      "1144\n",
      "1082\n",
      "1.057301293900185\n"
     ]
    }
   ],
   "source": [
    "# NUMBERS DISCUSSED IN SECTION III (Measure III) \n",
    "import numpy as np\n",
    "print(np.sum(replsumtreated.to_list()[0:6]))\n",
    "print(np.sum(replsumnottreated.to_list()[0:6]))\n",
    "print(np.sum(replsumtreated.to_list()[0:6])/np.sum(replsumnottreated.to_list()[0:6]))\n",
    "\n",
    "print(np.sum(replsumtreated.to_list()[7:13]))\n",
    "print(np.sum(replsumnottreated.to_list()[7:13]))\n",
    "print(np.sum(replsumtreated.to_list()[7:13])/np.sum(replsumnottreated.to_list()[7:13]))\n",
    "\n",
    "print(np.sum(replsumtreated.to_list()[14:18]))\n",
    "print(np.sum(replsumnottreated.to_list()[14:18]))\n",
    "print(np.sum(replsumtreated.to_list()[14:18])/np.sum(replsumnottreated.to_list()[14:18]))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2002     0\n",
      "2003     1\n",
      "2004     0\n",
      "2005     0\n",
      "2006     1\n",
      "2007     0\n",
      "2008     2\n",
      "2009    12\n",
      "2010    23\n",
      "2011    34\n",
      "2012    10\n",
      "2101    13\n",
      "2102    15\n",
      "2103    12\n",
      "2104    12\n",
      "2105    12\n",
      "2106     9\n",
      "2107     9\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2002     0\n",
       "2003     0\n",
       "2004     0\n",
       "2005     0\n",
       "2006     0\n",
       "2007     0\n",
       "2008     0\n",
       "2009     0\n",
       "2010     1\n",
       "2011     0\n",
       "2012     0\n",
       "2101     0\n",
       "2102     0\n",
       "2103     9\n",
       "2104    25\n",
       "2105    19\n",
       "2106    10\n",
       "2107     9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now preparation for TABLE 3 and looking at monthly data\n",
    "\n",
    "import pandas as pd\n",
    "treatedpanel=pd.DataFrame(columns=[2002,2003,2004,2005,2006,2007,2008, 2009,2010,2011,2012,2101,2102,2103,2104,2105,2106,2107])\n",
    "treatedpanel2=pd.DataFrame(columns=[2002,2003,2004,2005,2006,2007,2008, 2009,2010,2011,2012,2101,2102,2103,2104,2105,2106,2107])\n",
    "\n",
    "treatedpanelback=pd.DataFrame(columns=[2002,2003,2004,2005,2006,2007,2008, 2009,2010,2011,2012,2101,2102,2103,2104,2105,2106,2107])\n",
    "treatedpanel2back=pd.DataFrame(columns=[2002,2003,2004,2005,2006,2007,2008, 2009,2010,2011,2012,2101,2102,2103,2104,2105,2106,2107])\n",
    "\n",
    "\n",
    "for i in [2002,2003,2004,2005,2006,2007,2008, 2009,2010,2011,2012,2101,2102,2103,2104,2105,2106,2107]:\n",
    "    df = pd.read_csv(\n",
    "        str(i)+'.txt', sep=\" \",header=None)\n",
    "   \n",
    "    treatedpanel[i]=pd.to_numeric(df[3].loc[0:160].str.replace(\",\",\"\"))\n",
    "    treatedpanel2[i]=pd.to_numeric(df[3].loc[162:324].str.replace(\",\",\"\"))\n",
    "    treatedpanelback[i]=pd.to_numeric(df[5].loc[0:160]) # these are numbers directly, no need to replace a ,\n",
    "    treatedpanel2back[i]=pd.to_numeric(df[5].loc[162:324])\n",
    "\n",
    "\n",
    "print(treatedpanel.sum(axis=0))\n",
    "treatedpanel2.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete 55\n",
    "treatedpanel=treatedpanel.drop([55]).reset_index(drop=True)\n",
    "treatedpanelback=treatedpanelback.drop([55]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2101</th>\n",
       "      <th>2102</th>\n",
       "      <th>2103</th>\n",
       "      <th>2104</th>\n",
       "      <th>2105</th>\n",
       "      <th>2106</th>\n",
       "      <th>2107</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>323 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     2002  2003  2004  2005  2006  2007  2008  2009  2010  2011  2012  2101  \\\n",
       "0     0.0   0.0   0.0   0.0   0.0   0.0   0.0   1.0   0.0   0.0   0.0   0.0   \n",
       "1     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "2     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "3     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "4     0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "..    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "318   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "319   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "320   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "321   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "322   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
       "\n",
       "     2102  2103  2104  2105  2106  2107  \n",
       "0     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "1     0.0   0.0   0.0   0.0   0.0   1.0  \n",
       "2     0.0   0.0   0.0   0.0   1.0   0.0  \n",
       "3     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "4     0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "..    ...   ...   ...   ...   ...   ...  \n",
       "318   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "319   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "320   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "321   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "322   0.0   0.0   0.0   1.0   0.0   0.0  \n",
       "\n",
       "[323 rows x 18 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatedpanel2['new index']=list(range(160,323))\n",
    "treatedpanel2=treatedpanel2.set_index('new index')\n",
    "panel=pd.concat([treatedpanel,treatedpanel2], axis=0)\n",
    "panel\n",
    "\n",
    "treatedpanel2back['new index']=list(range(160,323))\n",
    "treatedpanel2back=treatedpanel2back.set_index('new index')\n",
    "panelback=pd.concat([treatedpanelback,treatedpanel2back], axis=0)\n",
    "panelback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding cluster ids\n",
    "panel['clusters']=data['clusters']\n",
    "panel['randomized']=data['randomized']\n",
    "\n",
    "panelback['clusters']=data['clusters']\n",
    "panelback['randomized']=data['randomized']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      41\n",
       "1      31\n",
       "2      15\n",
       "3       4\n",
       "4       4\n",
       "       ..\n",
       "318     1\n",
       "319    13\n",
       "320     8\n",
       "321     1\n",
       "322     7\n",
       "Name: 1, Length: 323, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100\n",
       "1     38\n",
       "2      9\n",
       "3      7\n",
       "4      3\n",
       "5      1\n",
       "6      1\n",
       "7      1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "panel['sum0']=data[0]\n",
    "np.sum(panel[[2009,2010,2011,2012,2101,2102]].loc[panel['randomized']==1], axis=1).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(panel['sum0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute differences in clicks by cluster\n",
    "alldifsplacebobefore=[]\n",
    "alldifsplaceboafter=[]\n",
    "alldifs=[]\n",
    "alldifs6=[]\n",
    "\n",
    "for i in range(1,68):\n",
    "    \n",
    "    a=panel[panel['clusters']==i]\n",
    "    for j in [2009,2010,2011,2012,2101,2102]:\n",
    "        meantreat=np.mean(a[a['randomized']==1][j])\n",
    "        meancontrol=np.mean(a[a['randomized']==0][j])\n",
    "        alldifs+=[meantreat-meancontrol]\n",
    "        \n",
    "    for j in [2002,2003,2004,2005,2006,2007]:\n",
    "        meantreat=np.mean(a[a['randomized']==1][j])\n",
    "        meancontrol=np.mean(a[a['randomized']==0][j])\n",
    "        alldifsplacebobefore+=[meantreat-meancontrol]\n",
    "        \n",
    "    for j in [2104,2105,2106,2107]:\n",
    "        meantreat=np.mean(a[a['randomized']==1][j])\n",
    "        meancontrol=np.mean(a[a['randomized']==0][j])\n",
    "        alldifsplaceboafter+=[meantreat-meancontrol]\n",
    "\n",
    "    b=a[a['randomized']==1]\n",
    "    c=b[[2009,2010,2011,2012,2101,2102]]\n",
    "    meantreat=np.mean(c.sum(axis=1))\n",
    "    b=a[a['randomized']==0]\n",
    "    c=b[[2009,2010,2011,2012,2101,2102]]\n",
    "    meancontrol=np.mean(c.sum(axis=1))\n",
    "    alldifs6+=[meantreat-meancontrol]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "402\n",
      "0.11090381426202321\n",
      "0.0\n",
      "402\n",
      "0.0024875621890547263\n",
      "0.0\n",
      "268\n",
      "-0.03824626865671642\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# to get the stats\n",
    "print(len(alldifs))\n",
    "#print(min(alldifs))\n",
    "#print(max(alldifs))\n",
    "\n",
    "print(np.mean(alldifs))\n",
    "\n",
    "print(np.median(alldifs))\n",
    "\n",
    "print(len(alldifsplacebobefore))\n",
    "#print(min(alldifsplacebobefore))\n",
    "#print(max(alldifsplacebobefore))\n",
    "print(np.mean(alldifsplacebobefore))\n",
    "\n",
    "\n",
    "print(np.median(alldifsplacebobefore))\n",
    "\n",
    "\n",
    "print(len(alldifsplaceboafter))\n",
    "#print(min(alldifsplaceboafter))\n",
    "#print(max(alldifsplaceboafter))\n",
    "print(np.mean(alldifsplaceboafter))\n",
    "\n",
    "print(np.median(alldifsplaceboafter))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1.0\n",
      "1      1.0\n",
      "2      1.0\n",
      "3      1.0\n",
      "4      1.0\n",
      "      ... \n",
      "397    1.0\n",
      "398    1.0\n",
      "399    1.0\n",
      "400    1.0\n",
      "401    1.0\n",
      "Length: 402, dtype: float64\n",
      "0      0.000000\n",
      "1      0.000000\n",
      "2      0.500000\n",
      "3      0.000000\n",
      "4      0.000000\n",
      "         ...   \n",
      "397    0.333333\n",
      "398    0.333333\n",
      "399    0.000000\n",
      "400    0.000000\n",
      "401    0.333333\n",
      "Length: 402, dtype: float64\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                       nan\n",
      "Date:                Thu, 20 Apr 2023   Prob (F-statistic):                nan\n",
      "Time:                        21:41:04   Log-Likelihood:                -14.397\n",
      "No. Observations:                 402   AIC:                             30.79\n",
      "Df Residuals:                     401   BIC:                             34.79\n",
      "Df Model:                           0                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.1109      0.013      8.855      0.000       0.086       0.135\n",
      "==============================================================================\n",
      "Omnibus:                      261.148   Durbin-Watson:                   1.572\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2040.355\n",
      "Skew:                           2.814   Prob(JB):                         0.00\n",
      "Kurtosis:                      12.495   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
     ]
    }
   ],
   "source": [
    "# to get mean and confidence interval of TABLE 3 - (1)\n",
    "# based on difference in group1 treatment period - September 2020 February 2021\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "x = pd.Series(np.ones(len(alldifs)))\n",
    "y = pd.Series(alldifs) # let's also put sales in panda series \n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "model = sm.OLS(y, x, missing='drop')\n",
    "results = model.fit(cov_type='HC1')\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         QuantReg Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   Pseudo R-squared:           -1.377e-06\n",
      "Model:                       QuantReg   Bandwidth:                       0.000\n",
      "Method:                 Least Squares   Sparsity:                          nan\n",
      "Date:                Thu, 20 Apr 2023   No. Observations:                  402\n",
      "Time:                        21:41:05   Df Residuals:                      401\n",
      "                                        Df Model:                            0\n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   2.602e-07        nan        nan        nan         nan         nan\n",
      "==============================================================================\n",
      "                         QuantReg Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   Pseudo R-squared:           -1.823e-06\n",
      "Model:                       QuantReg   Bandwidth:                       0.000\n",
      "Method:                 Least Squares   Sparsity:                          nan\n",
      "Date:                Thu, 20 Apr 2023   No. Observations:                  402\n",
      "Time:                        21:41:05   Df Residuals:                      401\n",
      "                                        Df Model:                            0\n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   3.484e-06        nan        nan        nan         nan         nan\n",
      "==============================================================================\n",
      "                         QuantReg Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   Pseudo R-squared:           -1.700e-06\n",
      "Model:                       QuantReg   Bandwidth:                       0.000\n",
      "Method:                 Least Squares   Sparsity:                          nan\n",
      "Date:                Thu, 20 Apr 2023   No. Observations:                  402\n",
      "Time:                        21:41:05   Df Residuals:                      401\n",
      "                                        Df Model:                            0\n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   8.673e-08        nan        nan        nan         nan         nan\n",
      "==============================================================================\n",
      "0      1.0\n",
      "1      1.0\n",
      "2      1.0\n",
      "3      1.0\n",
      "4      1.0\n",
      "      ... \n",
      "199    1.0\n",
      "200    1.0\n",
      "201    1.0\n",
      "202    1.0\n",
      "203    1.0\n",
      "Length: 204, dtype: float64\n",
      "0      0.0\n",
      "1      0.0\n",
      "2      0.5\n",
      "3      0.0\n",
      "4      0.0\n",
      "      ... \n",
      "199    0.0\n",
      "200    0.0\n",
      "201    0.0\n",
      "202    0.0\n",
      "203    0.0\n",
      "Length: 204, dtype: float64\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                       nan\n",
      "Date:                Thu, 20 Apr 2023   Prob (F-statistic):                nan\n",
      "Time:                        21:41:05   Log-Likelihood:                 96.119\n",
      "No. Observations:                 204   AIC:                            -190.2\n",
      "Df Residuals:                     203   BIC:                            -186.9\n",
      "Df Model:                           0                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.0543      0.011      5.125      0.000       0.034       0.075\n",
      "==============================================================================\n",
      "Omnibus:                      124.444   Durbin-Watson:                   1.877\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              509.445\n",
      "Skew:                           2.643   Prob(JB):                    2.37e-111\n",
      "Kurtosis:                       8.656   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n",
      "0      1.0\n",
      "1      1.0\n",
      "2      1.0\n",
      "3      1.0\n",
      "4      1.0\n",
      "      ... \n",
      "193    1.0\n",
      "194    1.0\n",
      "195    1.0\n",
      "196    1.0\n",
      "197    1.0\n",
      "Length: 198, dtype: float64\n",
      "0      0.000000\n",
      "1      0.000000\n",
      "2      0.000000\n",
      "3      0.000000\n",
      "4      0.000000\n",
      "         ...   \n",
      "193    0.333333\n",
      "194    0.333333\n",
      "195    0.000000\n",
      "196    0.000000\n",
      "197    0.333333\n",
      "Length: 198, dtype: float64\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                      -0.000\n",
      "Model:                            OLS   Adj. R-squared:                 -0.000\n",
      "Method:                 Least Squares   F-statistic:                       nan\n",
      "Date:                Thu, 20 Apr 2023   Prob (F-statistic):                nan\n",
      "Time:                        21:41:05   Log-Likelihood:                -50.484\n",
      "No. Observations:                 198   AIC:                             103.0\n",
      "Df Residuals:                     197   BIC:                             106.3\n",
      "Df Model:                           0                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.1692      0.022      7.605      0.000       0.126       0.213\n",
      "==============================================================================\n",
      "Omnibus:                      105.034   Durbin-Watson:                   1.606\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              401.267\n",
      "Skew:                           2.222   Prob(JB):                     7.34e-88\n",
      "Kurtosis:                       8.374   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda/lib/python3.9/site-packages/statsmodels/regression/quantile_regression.py:203: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  fhat0 = 1. / (nobs * h) * np.sum(kernel(e / h))\n",
      "/opt/miniconda/lib/python3.9/site-packages/statsmodels/regression/quantile_regression.py:203: RuntimeWarning: divide by zero encountered in divide\n",
      "  fhat0 = 1. / (nobs * h) * np.sum(kernel(e / h))\n",
      "/opt/miniconda/lib/python3.9/site-packages/statsmodels/regression/quantile_regression.py:236: RuntimeWarning: invalid value encountered in multiply\n",
      "  kernels['epa'] = lambda u: 3. / 4 * (1-u**2) * np.where(np.abs(u) <= 1, 1, 0)\n",
      "/opt/miniconda/lib/python3.9/site-packages/statsmodels/regression/quantile_regression.py:203: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  fhat0 = 1. / (nobs * h) * np.sum(kernel(e / h))\n",
      "/opt/miniconda/lib/python3.9/site-packages/statsmodels/regression/quantile_regression.py:203: RuntimeWarning: divide by zero encountered in divide\n",
      "  fhat0 = 1. / (nobs * h) * np.sum(kernel(e / h))\n",
      "/opt/miniconda/lib/python3.9/site-packages/statsmodels/regression/quantile_regression.py:236: RuntimeWarning: invalid value encountered in multiply\n",
      "  kernels['epa'] = lambda u: 3. / 4 * (1-u**2) * np.where(np.abs(u) <= 1, 1, 0)\n",
      "/opt/miniconda/lib/python3.9/site-packages/statsmodels/regression/quantile_regression.py:203: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  fhat0 = 1. / (nobs * h) * np.sum(kernel(e / h))\n",
      "/opt/miniconda/lib/python3.9/site-packages/statsmodels/regression/quantile_regression.py:203: RuntimeWarning: divide by zero encountered in divide\n",
      "  fhat0 = 1. / (nobs * h) * np.sum(kernel(e / h))\n",
      "/opt/miniconda/lib/python3.9/site-packages/statsmodels/regression/quantile_regression.py:236: RuntimeWarning: invalid value encountered in multiply\n",
      "  kernels['epa'] = lambda u: 3. / 4 * (1-u**2) * np.where(np.abs(u) <= 1, 1, 0)\n"
     ]
    }
   ],
   "source": [
    "# for second revision: check quantile regression\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "data1= pd.DataFrame()\n",
    "data1=y\n",
    "mod = smf.quantreg(\"y ~ 1\", data1)\n",
    "res = mod.fit(q=0.5)\n",
    "print(res.summary())\n",
    "mod = smf.quantreg(\"y ~ 1\", data1)\n",
    "res = mod.fit(q=0.75)\n",
    "print(res.summary())\n",
    "mod = smf.quantreg(\"y ~ 1\", data1)\n",
    "res = mod.fit(q=0.25)\n",
    "print(res.summary())\n",
    "\n",
    "# since cluster numbers are correlated with the expected nr of citations\n",
    "# i split up the sample into 2 - the second regression is for the observations \n",
    "# with on average bigger citations \n",
    "x = pd.Series(np.ones(len(alldifs[0:204])))\n",
    "y = pd.Series(alldifs[0:204]) # let's also put sales in panda series \n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "model = sm.OLS(y, x, missing='drop')\n",
    "results = model.fit(cov_type='HC1')\n",
    "print(results.summary())\n",
    "\n",
    "x = pd.Series(np.ones(len(alldifs[204:403])))\n",
    "y = pd.Series(alldifs[204:403]) # let's also put sales in panda series \n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "model = sm.OLS(y, x, missing='drop')\n",
    "results = model.fit(cov_type='HC1')\n",
    "print(results.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1.0\n",
      "1      1.0\n",
      "2      1.0\n",
      "3      1.0\n",
      "4      1.0\n",
      "      ... \n",
      "263    1.0\n",
      "264    1.0\n",
      "265    1.0\n",
      "266    1.0\n",
      "267    1.0\n",
      "Length: 268, dtype: float64\n",
      "0      0.000000\n",
      "1      0.000000\n",
      "2      0.000000\n",
      "3      0.000000\n",
      "4      0.000000\n",
      "         ...   \n",
      "263   -0.500000\n",
      "264   -0.666667\n",
      "265   -0.333333\n",
      "266    0.000000\n",
      "267   -0.666667\n",
      "Length: 268, dtype: float64\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                       nan\n",
      "Date:                Thu, 20 Apr 2023   Prob (F-statistic):                nan\n",
      "Time:                        21:47:49   Log-Likelihood:                -116.26\n",
      "No. Observations:                 268   AIC:                             234.5\n",
      "Df Residuals:                     267   BIC:                             238.1\n",
      "Df Model:                           0                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         -0.0382      0.023     -1.674      0.094      -0.083       0.007\n",
      "==============================================================================\n",
      "Omnibus:                      205.999   Durbin-Watson:                   1.405\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             6020.939\n",
      "Skew:                          -2.684   Prob(JB):                         0.00\n",
      "Kurtosis:                      25.591   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
     ]
    }
   ],
   "source": [
    "# to get mean and confidence interval of TABLE 3 - (2)\n",
    "# based on difference in final period April - July 2021\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "x = pd.Series(np.ones(len(alldifsplaceboafter)))\n",
    "y = pd.Series(alldifsplaceboafter) # let's also put sales in panda series \n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "model = sm.OLS(y, x, missing='drop')\n",
    "results = model.fit(cov_type='HC1')\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.0\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# median bootstrap for TABLE 3 - rows (3) and (4\n",
    "medians=[]\n",
    "for i in range(1,10000):\n",
    "    # FOR 3\n",
    "    #medians+=[np.median(np.random.choice(alldifs, len(alldifs), replace = True))]\n",
    "    \n",
    "    # FOR 4\n",
    "    medians+=[np.median(np.random.choice(alldifsplaceboafter, len(alldifsplaceboafter), replace = True))] \n",
    "\n",
    "print(np.mean(medians))\n",
    "print(np.percentile(medians,5))\n",
    "print(np.percentile(medians,95))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.101041666666666\n",
      "3.0\n",
      "960\n",
      "0.16562500000000002\n",
      "0.025\n",
      "0.0\n",
      "960\n",
      "0.9770833333333333\n",
      "7.076041666666667\n",
      "3.0\n",
      "0.3375\n",
      "0.328125\n",
      "0.16666666666666663\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_567091/3644065802.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  origvisitsmod['clusters']=data['clusters']\n",
      "/tmp/ipykernel_567091/3644065802.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  origvisitsmod['randomized']=data['randomized']\n"
     ]
    }
   ],
   "source": [
    "# for TABLE 3, descriptive stats of originals for treated in group 1 (5)-(7)\n",
    "\n",
    "# first get the origin visits\n",
    "origvisitsmod=origvisits[['orig2020-09','orig2020-10','orig2020-11','orig2020-12','orig2021-01','orig2021-02']]\n",
    "origvisitsmod['clusters']=data['clusters']\n",
    "origvisitsmod['randomized']=data['randomized']\n",
    "a=origvisitsmod[origvisitsmod['randomized']==1]\n",
    "meantreatlist=[]\n",
    "for j in ['orig2020-09','orig2020-10','orig2020-11','orig2020-12','orig2021-01','orig2021-02']:\n",
    "    meantreatlist+=a[j].to_list()\n",
    "\n",
    "print(np.mean(meantreatlist))\n",
    "print(np.median(meantreatlist))\n",
    "print(len(meantreatlist))\n",
    "print(1-np.sum(pd.Series(meantreatlist)>0)/len(meantreatlist))\n",
    "\n",
    "# to be able to subtract the ones coming from the replication pages\n",
    "meantreatbacklist=[]\n",
    "b=panelback[panelback['randomized']==1]   \n",
    "for j in [2009,2010,2011,2012,2101,2102]:\n",
    "    meantreatbacklist+=b[j].to_list()\n",
    "    \n",
    "print(np.mean(meantreatbacklist))\n",
    "print(np.median(meantreatbacklist))\n",
    "print(len(meantreatbacklist))\n",
    "print(1-np.sum(pd.Series(meantreatbacklist)>0)/len(meantreatbacklist))\n",
    "\n",
    "# to get the difference\n",
    "print(np.mean(pd.Series(meantreatlist)-pd.Series(meantreatbacklist)))\n",
    "print(np.median(pd.Series(meantreatlist)-pd.Series(meantreatbacklist)))\n",
    "\n",
    "print(np.sum((pd.Series(meantreatlist)-pd.Series(meantreatbacklist))>5)/len(meantreatbacklist))\n",
    "\n",
    "print(np.sum((pd.Series(meantreatlist)-pd.Series(meantreatbacklist))<2)/len(meantreatbacklist))\n",
    "\n",
    "print(1-np.sum((pd.Series(meantreatlist)-pd.Series(meantreatbacklist))>0)/len(meantreatbacklist))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now bottom part of TABLE 3\n",
    "\n",
    "replvisitsmod=replvisits.copy()\n",
    "replvisitsmod['clusters']=data['clusters']\n",
    "replvisitsmod['randomized']=data['randomized']\n",
    "alldifs=[]\n",
    "alldifs6=[]\n",
    "alldifsplacebobefore=[]\n",
    "alldifsplaceboafter=[]\n",
    "checktreat=[]\n",
    "checkcontrol=[]\n",
    "\n",
    "for i in range(1,68):\n",
    "    \n",
    "    a=replvisitsmod[replvisitsmod['clusters']==i]\n",
    "    for j in ['orig2020-09','orig2020-10','orig2020-11','orig2020-12','orig2021-01','orig2021-02']:\n",
    "        meantreat=np.mean(a[a['randomized']==1][j])\n",
    "        meancontrol=np.mean(a[a['randomized']==0][j])\n",
    "        alldifs+=[meantreat-meancontrol]\n",
    "        checktreat+=[meantreat]\n",
    "        checkcontrol+=[meancontrol]\n",
    "    for j in ['orig2020-02','orig2020-03','orig2020-04','orig2020-05','orig2020-06','orig2020-07']:\n",
    "        meantreat=np.mean(a[a['randomized']==1][j])\n",
    "        meancontrol=np.mean(a[a['randomized']==0][j])\n",
    "        alldifsplacebobefore+=[meantreat-meancontrol]\n",
    "        \n",
    "    for j in ['orig2021-04','orig2021-05','orig2021-06','orig2021-07']:\n",
    "        meantreat=np.mean(a[a['randomized']==1][j])\n",
    "        meancontrol=np.mean(a[a['randomized']==0][j])\n",
    "        alldifsplaceboafter+=[meantreat-meancontrol]\n",
    "\n",
    "      \n",
    "        \n",
    "        \n",
    "        \n",
    "    b=a[a['randomized']==1]\n",
    "    c=b[['orig2020-09','orig2020-10','orig2020-11','orig2020-12','orig2021-01','orig2021-02']]\n",
    "    meantreat=np.mean(c.sum(axis=1))\n",
    "    b=a[a['randomized']==0]\n",
    "    c=b[['orig2020-09','orig2020-10','orig2020-11','orig2020-12','orig2021-01','orig2021-02']]\n",
    "    meancontrol=np.mean(c.sum(axis=1))\n",
    "    alldifs6+=[meantreat-meancontrol]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "402\n",
      "0.23507462686567165\n",
      "0.0\n",
      "402\n",
      "0.050995024875621874\n",
      "0.0\n",
      "268\n",
      "0.12810945273631832\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "# to get the stats\n",
    "\n",
    "print(len(alldifs))\n",
    "#print(min(alldifs))\n",
    "#print(max(alldifs))\n",
    "\n",
    "print(np.mean(alldifs))\n",
    "\n",
    "print(np.median(alldifs))\n",
    "\n",
    "print(len(alldifsplacebobefore))\n",
    "#print(min(alldifsplacebobefore))\n",
    "#print(max(alldifsplacebobefore))\n",
    "print(np.mean(alldifsplacebobefore))\n",
    "\n",
    "\n",
    "print(np.median(alldifsplacebobefore))\n",
    "\n",
    "\n",
    "print(len(alldifsplaceboafter))\n",
    "#print(min(alldifsplaceboafter))\n",
    "#print(max(alldifsplaceboafter))\n",
    "print(np.mean(alldifsplaceboafter))\n",
    "\n",
    "print(np.median(alldifsplaceboafter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1.0\n",
      "1      1.0\n",
      "2      1.0\n",
      "3      1.0\n",
      "4      1.0\n",
      "      ... \n",
      "397    1.0\n",
      "398    1.0\n",
      "399    1.0\n",
      "400    1.0\n",
      "401    1.0\n",
      "Length: 402, dtype: float64\n",
      "0       0.000000\n",
      "1       0.000000\n",
      "2       0.500000\n",
      "3       0.000000\n",
      "4      -0.500000\n",
      "         ...    \n",
      "397    15.666667\n",
      "398    13.333333\n",
      "399     8.333333\n",
      "400    13.666667\n",
      "401    15.000000\n",
      "Length: 402, dtype: float64\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                       nan\n",
      "Date:                Thu, 20 Apr 2023   Prob (F-statistic):                nan\n",
      "Time:                        21:52:13   Log-Likelihood:                -1029.2\n",
      "No. Observations:                 402   AIC:                             2060.\n",
      "Df Residuals:                     401   BIC:                             2064.\n",
      "Df Model:                           0                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.2351      0.156      1.504      0.133      -0.071       0.541\n",
      "==============================================================================\n",
      "Omnibus:                      189.602   Durbin-Watson:                   0.600\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3403.285\n",
      "Skew:                           1.539   Prob(JB):                         0.00\n",
      "Kurtosis:                      16.918   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
     ]
    }
   ],
   "source": [
    "# to get mean and confidence interval of TABLE 3 - (8)\n",
    "# based on difference in group1 treatment period - September 2020 February 2021\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "x = pd.Series(np.ones(len(alldifs)))\n",
    "y = pd.Series(alldifs) # let's also put sales in panda series \n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "model = sm.OLS(y, x, missing='drop')\n",
    "results = model.fit(cov_type='HC1')\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1.0\n",
      "1      1.0\n",
      "2      1.0\n",
      "3      1.0\n",
      "4      1.0\n",
      "      ... \n",
      "263    1.0\n",
      "264    1.0\n",
      "265    1.0\n",
      "266    1.0\n",
      "267    1.0\n",
      "Length: 268, dtype: float64\n",
      "0       0.500000\n",
      "1       1.000000\n",
      "2       0.000000\n",
      "3       0.500000\n",
      "4      -0.500000\n",
      "         ...    \n",
      "263   -11.000000\n",
      "264    11.666667\n",
      "265    11.666667\n",
      "266    11.333333\n",
      "267    13.333333\n",
      "Length: 268, dtype: float64\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.000\n",
      "Model:                            OLS   Adj. R-squared:                  0.000\n",
      "Method:                 Least Squares   F-statistic:                       nan\n",
      "Date:                Thu, 20 Apr 2023   Prob (F-statistic):                nan\n",
      "Time:                        21:52:30   Log-Likelihood:                -704.84\n",
      "No. Observations:                 268   AIC:                             1412.\n",
      "Df Residuals:                     267   BIC:                             1415.\n",
      "Df Model:                           0                                         \n",
      "Covariance Type:                  HC1                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const          0.1281      0.205      0.624      0.533      -0.275       0.531\n",
      "==============================================================================\n",
      "Omnibus:                       76.895   Durbin-Watson:                   0.884\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             2489.839\n",
      "Skew:                           0.293   Prob(JB):                         0.00\n",
      "Kurtosis:                      17.921   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors are heteroscedasticity robust (HC1)\n"
     ]
    }
   ],
   "source": [
    "# to get mean and confidence interval of TABLE 3 - (9)\n",
    "# based on difference in group1 treatment period - September 2020 February 2021\n",
    "import statsmodels.api as sm\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "x = pd.Series(np.ones(len(alldifsplaceboafter)))\n",
    "y = pd.Series(alldifsplaceboafter) # let's also put sales in panda series \n",
    "\n",
    "print(x)\n",
    "print(y)\n",
    "model = sm.OLS(y, x, missing='drop')\n",
    "results = model.fit(cov_type='HC1')\n",
    "print(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.012722105543887717\n",
      "0.0\n",
      "0.16666666666666657\n"
     ]
    }
   ],
   "source": [
    "# median bootstrap for TABLE 3 - rows (10) and (11)\n",
    "import random\n",
    "\n",
    "random.seed(10)\n",
    "\n",
    "medians=[]\n",
    "for i in range(1,10000):\n",
    "    medians+=[np.median(np.random.choice(alldifs, len(alldifs), replace = True))]\n",
    "    #medians+=[np.median(np.random.choice(alldifsplaceboafter, len(alldifsplaceboafter), replace = True))]\n",
    "print(np.mean(medians))\n",
    "print(np.percentile(medians,5))\n",
    "print(np.percentile(medians,95))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1.7645833333333334\n",
      "1.0\n",
      "960\n",
      "0.4552083333333333\n",
      "0.11145833333333334\n",
      "0.0\n",
      "960\n",
      "0.9083333333333333\n",
      "1.653125\n",
      "1.0\n",
      "0.4854166666666667\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_567091/3170582115.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  replvisitsmod['clusters']=data['clusters']\n",
      "/tmp/ipykernel_567091/3170582115.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  replvisitsmod['randomized']=data['randomized']\n"
     ]
    }
   ],
   "source": [
    "# to get stats on replvisits for TABLE3  12-14\n",
    "replvisitsmod=replvisits[['orig2020-09','orig2020-10','orig2020-11','orig2020-12','orig2021-01','orig2021-02']]\n",
    "replvisitsmod['clusters']=data['clusters']\n",
    "replvisitsmod['randomized']=data['randomized']\n",
    "a=replvisitsmod[replvisitsmod['randomized']==1]\n",
    "meantreatlist=[]\n",
    "for j in ['orig2020-09','orig2020-10','orig2020-11','orig2020-12','orig2021-01','orig2021-02']:\n",
    "    meantreatlist+=a[j].to_list()\n",
    "\n",
    "print(np.sum(np.isnan(pd.Series(meantreatlist))))\n",
    "# there are a few missing values \n",
    "print(np.nanmean(meantreatlist))\n",
    "print(np.nanmedian(meantreatlist))\n",
    "print(len(meantreatlist))\n",
    "print(1-np.sum(pd.Series(meantreatlist)>0)/len(meantreatlist))\n",
    "\n",
    "\n",
    "# to be able to subtract the ones coming from the originals pages\n",
    "meantreatclicklist=[]\n",
    "b=panel[panel['randomized']==1]   \n",
    "for j in [2009,2010,2011,2012,2101,2102]:\n",
    "    meantreatclicklist+=b[j].to_list()\n",
    "    \n",
    "print(np.mean(meantreatclicklist))\n",
    "print(np.median(meantreatclicklist))\n",
    "print(len(meantreatclicklist))\n",
    "print(1-np.sum(pd.Series(meantreatclicklist)>0)/len(meantreatclicklist))\n",
    "\n",
    "# to get the difference\n",
    "print(np.mean(pd.Series(meantreatlist)-pd.Series(meantreatclicklist)))\n",
    "print(np.median(pd.Series(meantreatlist)-pd.Series(meantreatclicklist)))\n",
    "\n",
    "print(1-np.sum((pd.Series(meantreatlist)-pd.Series(meantreatclicklist))>0)/len(meantreatclicklist))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  }
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